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85",{},{"id":536,"createTime":537,"updateTime":538,"relativeEntities":539,"slug":540,"properties":541,"entityType":45,"verifyStatus":334,"verifyTime":554,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":555,"fullTextUrl":556,"authors":557,"publicationType":109,"publisherRelationship":602,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":620,"lastCitationAnalyze":621,"indexDatabases":622,"openAccess":18,"references":18,"isForceReanalyzing":315},"204e2efe-10fe-48dc-80d3-9e101ca9bfdb","2023-12-24T03:34:25.163+00:00","2026-02-18T12:34:39.259+00:00",[],"Modified-Gath-Geva-fuzzy-clustering-for-identification-of-Takagi-Sugeno-fuzzy-models",{"abstract":542,"title":544,"gsPaper":546,"keywords":548,"references":550,"doi":552},{"EN":543},"The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.",{"EN":545},"Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models",{"VOID":547},"[]",{"EN":549},"Takagi-Sugeno model,Fuzzy sets,Fuzzy systems,Optimization methods,Clustering algorithms,Partitioning algorithms,Input variables,Multidimensional systems,Predictive models,Nonlinear systems",{"VOID":551},"10.1007\u002F978-3-642-60767-7\nhoppner, 1999, Fuzzy Cluster Analysis&#x2014 Methods for Classification Data Analysis and Image Recognition\n10.1109\u002FFUZZY.1996.552396\njang, 1997, Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence\n10.1109\u002F5.364486\n10.1109\u002F91.842154\njohansen, 2002, on multi-objective identification of takagi-sugeno fuzzy model parameters, Preprints 15th IFAC World Congress\n10.1109\u002F91.855918\nkambhatala, 1996, Local models and Gaussian mixture models for statistical data processing\n10.1109\u002F91.728458\n10.1109\u002FT-C.1975.224317\n10.1007\u002F978-3-642-60767-7_2\ndraper, 1994, Applied regression analysis\nbishop, 1995, Neural Networks for Pattern Recognition\n10.1038\u002F16873\n10.1109\u002F34.192473\nbabus˘ka babuska, 1998, Fuzzy Modeling for Control, 10.1007\u002F978-94-011-4868-9\ngustafson, 1979, fuzzy clustering with fuzzy covariance matrix, Proc IEEE CDC, 761\nabonyi, 2000, local and global identification and interpretation of parameters in takagi-sugeno fuzzy models, Proc IEEE ICFS, 835\nmurray-smith, 1997, Multiple Model Approaches to Nonlinear Modeling and Control\n10.1016\u002F0165-0114(86)90010-2\n10.1109\u002FFUZZY.2000.839128\n10.1109\u002FTSMC.1985.6313399\n10.1109\u002FTFUZZ.1993.390281\n10.1109\u002F91.705503",{"VOID":553},"10.1109\u002FTSMCB.2002.1033180","2024-05-11T15:04:14.702+00:00","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033180\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033180",[558,574,589],{"id":559,"sortIndex":19,"researcher":18,"roles":560,"affiliations":562,"properties":571,"displayName":573,"givenName":18,"familyName":18},"21c97a32-1d9f-4448-b9ee-dbb6f1b12fe2",[561],"AUTHOR",[563],{"id":564,"sortIndex":19,"affiliation":565,"properties":18},"fa5f80c4-9e2d-4cd9-a5e9-4ed626852c46",{"id":564,"createTime":18,"updateTime":18,"relativeEntities":566,"slug":18,"properties":567,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":570,"statistic":18},[],{"title":568},{"VI":569},"Department of Process Engineering, University of Veszprem, Veszprem, Hungary",[],{"title":572},{"VI":573},"J. Abonyi",{"id":575,"sortIndex":75,"researcher":18,"roles":576,"affiliations":577,"properties":586,"displayName":588,"givenName":18,"familyName":18},"3675211d-0a20-4190-8653-37b74cc336c8",[561],[578],{"id":579,"sortIndex":19,"affiliation":580,"properties":18},"7152879d-22fb-4160-8dc7-e4b809d08606",{"id":579,"createTime":18,"updateTime":18,"relativeEntities":581,"slug":18,"properties":582,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":585,"statistic":18},[],{"title":583},{"VI":584},"Control Systems Engineering Group, Department of Information Technology and Systems, Delft University of Technnology, Delft, Netherlands",[],{"title":587},{"VI":588},"R. Babuska",{"id":590,"sortIndex":87,"researcher":18,"roles":591,"affiliations":592,"properties":599,"displayName":601,"givenName":18,"familyName":18},"7a922521-7ddf-4fc5-86bb-17864c0b87ea",[561],[593],{"id":564,"sortIndex":19,"affiliation":594,"properties":18},{"id":564,"createTime":18,"updateTime":18,"relativeEntities":595,"slug":18,"properties":596,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":598,"statistic":18},[],{"title":597},{"VI":569},[],{"title":600},{"VI":601},"F. Szeifert",{"url":555,"publisher":603,"properties":611},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":604,"slug":10,"properties":605,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":608,"manageAffiliations":609,"indexDatabases":610,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":606,"title":607},{"VOID":13},{"EN":15},[],[],[],{"issue":612,"pages":614,"volume":616},{"VOID":613},"5",{"VOID":615},"612-621",{"VOID":617},"32","2002-10-01",2002,"ERROR_IN_GET_PLATFORM_ID","2026-02-18T12:34:39.258+00:00",[],{"id":624,"createTime":625,"updateTime":626,"relativeEntities":627,"slug":628,"properties":629,"entityType":45,"verifyStatus":334,"verifyTime":626,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":640,"fullTextUrl":641,"authors":642,"publicationType":109,"publisherRelationship":703,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":717,"openAccess":18,"references":18,"isForceReanalyzing":315},"c6f0ac0d-093c-4132-aaa0-b71f3c38de5b","2023-12-24T03:36:00.145+00:00","2025-02-09T09:21:15.519+00:00",[],"Hierarchical-semi-numeric-method-for-pairwise-fuzzy-group-decision-making",{"abstract":630,"title":632,"keywords":634,"references":636,"doi":638},{"EN":631},"Gradual improvements to a single-level semi-numeric method, i.e., linguistic labels preference representation by fuzzy sets computation for pairwise fuzzy group decision making are summarized. The method is extended to solve multiple criteria hierarchical structure pairwise fuzzy group decision-making problems. The problems are hierarchically structured into focus, criteria, and alternatives. Decision makers express their evaluations of criteria and alternatives based on each criterion by using linguistic labels. The labels are converted into and processed in triangular fuzzy numbers (TFNs). Evaluations of criteria yield relative criteria weights. Evaluations of the alternatives, based on each criterion, yield a degree of preference for each alternative or a degree of satisfaction for each preference value. By using a neat ordered weighted average (OWA) or a fuzzy weighted average operator, solutions obtained based on each criterion are aggregated into final solutions. The hierarchical semi-numeric method is suitable for solving a larger and more complex pairwise fuzzy group decision-making problem. The proposed method has been verified and applied to solve some real cases and is compared to Saaty's (1996) analytic hierarchy process (AHP) method.",{"EN":633},"Hierarchical semi-numeric method for pairwise fuzzy group decision making",{"EN":635},"Decision making,Fuzzy sets,Computational modeling,Open wireless architecture,Mathematics,Art,Humans",{"VOID":637},"mon, 1993, fuzzy analytical hierarchy process by \u003Cformula> \u003Ctex>$\\alpha$\u003C\u002Ftex>\u003C\u002Fformula>-cut approach, Proc 5th IFSA World Congress, 627\nmarimin, 1990, difs: an expert system for selecting a distribution function, Ind J of Trop Agric, 2, 39\n10.1002\u002Fint.4550090403\n10.1109\u002F3477.662760\n10.1109\u002FCDC.1996.574266\nsaaty, 1980, The Analytic Hierarchy Process\n10.1016\u002F0165-0114(94)90344-1\n10.1080\u002F00207549108948085\n10.1109\u002FHICSS.1994.323485\n10.1016\u002F0165-0114(93)90194-M\nmarimin, 1997, nonnumeric method for pairwise fuzzy group decision making, J Intell Fuzzy Syst, 5, 257, 10.3233\u002FIFS-1997-5307\nsaaty, 1996, Decision Making with Dependence and Feedback The Analytic Network Process\n10.1016\u002F0165-0114(86)90014-X\n10.1002\u002Fmcda.4020020205\n10.1016\u002F0165-0114(81)90003-8\n10.1016\u002F0167-9236(88)90019-X\ntamura, 1997, on a descriptive analytic hierarchy process (d-ahp) for modeling the legitimacy of rank reversal, Proc Int Conf Methods and Applications of Multicriteria Decision Making, 82\n10.1016\u002F0165-0114(92)90107-F\nfedrizzi, 1994, consensual dynamics: an unsupervised learning model in group decision making, Proc Int Conf Fuzzy Logic Neural Nets and Soft Computing, 99\nfedrizzi, 1993, consensus degrees under fuzzy majorities and preferences using owa (ordered weighted average) operators, Proc 5th IFSA World Congress, 624\n10.1016\u002F0165-0114(79)90011-3\n10.1016\u002F0020-0255(75)90046-8\n10.1007\u002F978-94-009-9838-4_11\n10.1109\u002F21.87068\n10.1287\u002Fmnsc.32.7.841\n10.1080\u002F03081079408935212\n10.1109\u002F3468.553232\n10.1007\u002FBF01384404\n10.1016\u002F0305-0483(83)90047-6\n10.1016\u002F0148-2963(85)90043-8",{"VOID":639},"10.1109\u002FTSMCB.2002.1033190","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033190\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033190",[643,658,673,688],{"id":644,"sortIndex":19,"researcher":18,"roles":645,"affiliations":646,"properties":655,"displayName":657,"givenName":18,"familyName":18},"0a02167a-fe92-4780-a50a-17241380bce4",[561],[647],{"id":648,"sortIndex":19,"affiliation":649,"properties":18},"25e4d2a8-5001-4231-8d3d-3d200a98c876",{"id":648,"createTime":18,"updateTime":18,"relativeEntities":650,"slug":18,"properties":651,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":654,"statistic":18},[],{"title":652},{"VI":653},"Department of Agro-Industrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, Bogor, Indonesia",[],{"title":656},{"VI":657},"M. Marimin",{"id":659,"sortIndex":75,"researcher":18,"roles":660,"affiliations":661,"properties":670,"displayName":672,"givenName":18,"familyName":18},"5977acd4-5490-4f95-a647-036ef289af5d",[561],[662],{"id":663,"sortIndex":19,"affiliation":664,"properties":18},"0e48d871-65bc-4c70-b552-4213fb18643f",{"id":663,"createTime":18,"updateTime":18,"relativeEntities":665,"slug":18,"properties":666,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":669,"statistic":18},[],{"title":667},{"VI":668},"Department of Mathematics and Information Sciences, College of Integrated Arts and Sciences, Osaka Prefecture University, Osaka, Japan",[],{"title":671},{"VI":672},"M. Umano",{"id":674,"sortIndex":87,"researcher":18,"roles":675,"affiliations":676,"properties":685,"displayName":687,"givenName":18,"familyName":18},"d6ecb5f6-cf24-4b5b-b1c5-023bb6c621d4",[561],[677],{"id":678,"sortIndex":19,"affiliation":679,"properties":18},"1f1e8741-cb1e-4f29-9d48-3026df2c5ee3",{"id":678,"createTime":18,"updateTime":18,"relativeEntities":680,"slug":18,"properties":681,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":684,"statistic":18},[],{"title":682},{"VI":683},"Kobe University, Kobe, Japan",[],{"title":686},{"VI":687},"I. Hatono",{"id":689,"sortIndex":99,"researcher":18,"roles":690,"affiliations":691,"properties":700,"displayName":702,"givenName":18,"familyName":18},"f52e034d-a3b6-45d1-958d-f4adc9f53769",[561],[692],{"id":693,"sortIndex":19,"affiliation":694,"properties":18},"36521f4b-f1f2-4e58-a307-a064bb47721f",{"id":693,"createTime":18,"updateTime":18,"relativeEntities":695,"slug":18,"properties":696,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":699,"statistic":18},[],{"title":697},{"VI":698},"Department of Systems and Human Science, Graduate School of Engineering Science, Osaka University, Osaka, Japan",[],{"title":701},{"VI":702},"H. Tamura",{"url":640,"publisher":704,"properties":712},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":705,"slug":10,"properties":706,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":709,"manageAffiliations":710,"indexDatabases":711,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":707,"title":708},{"VOID":13},{"EN":15},[],[],[],{"issue":713,"pages":714,"volume":716},{"VOID":613},{"VOID":715},"691-700",{"VOID":617},[],{"id":719,"createTime":720,"updateTime":721,"relativeEntities":722,"slug":723,"properties":724,"entityType":45,"verifyStatus":334,"verifyTime":735,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":736,"fullTextUrl":737,"authors":738,"publicationType":109,"publisherRelationship":769,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":783,"openAccess":18,"references":18,"isForceReanalyzing":315},"9e25fab5-00d9-4c10-a39e-f061698f721a","2023-12-24T03:33:37.832+00:00","2025-01-28T14:57:05.826+00:00",[],"Defect-detection-in-textured-materials-using-optimized-filters",{"abstract":725,"title":727,"keywords":729,"references":731,"doi":733},{"EN":726},"The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. The performance of different feature separation criteria with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection.",{"EN":728},"Defect detection in textured materials using optimized filters",{"EN":730},"Finite impulse response filter,Inspection,Gabor filters,IIR filters,Fabrics,Information filtering,Information filters,Quality assurance,Textile industry,Production",{"VOID":732},"Özdemir, 1996, markov random fields and karhunen-loève transforms for defect inspection of textile products, Proc IEEE Conf Emerging Technologies and Factory Automation EFTA 96, 2, 697\n10.1109\u002FIAS.2001.955418\n10.1117\u002F1.601751\ncampbell, 1997, Flaw detection in woven textiles using space-dependent fourier transform\n10.1117\u002F1.601692\n10.1117\u002F1.600663\n10.1117\u002F1.601054\nvachtsevanos, 1998, Method and apparatus for analyzing an image to detect and identify defects\n10.1117\u002F1.1327837\n10.1109\u002F28.993164\nmead, 1978, Method for Automatic Fabric Inspection\n10.1109\u002F28.871274\n10.1016\u002F0143-8166(95)00044-O\n10.1109\u002F28.806035\n10.1109\u002FISIE.1999.796918\n10.1016\u002F0167-8655(84)90044-8\n10.1109\u002FICPR.1992.201654\nade, 1984, comparison of various filter sets for defect detection in textiles, Proc 7th Int Conf Pattern Recognit, 1, 428\n10.1109\u002FIECON.1993.339359\n10.1142\u002FS0218001496000554\n10.1016\u002FS0262-8856(99)00009-8\n10.1177\u002F004051759606600710\n10.1016\u002F0031-3203(91)90143-S\n10.1109\u002F21.105080\n10.1007\u002FBF01213639\nranden, 1996, optimal filtering for unsupervised texture feature extraction, Proc SPIE Conf Visual Communications and Image Process, 430\nranden, 1997, optimal texture filter design using feature extraction modeling, Working papers from H&#x2298 gskolen i Stavanger no 27\npapoulis, 1991, Probability Random Variables and Stochastic Processes 3rd ed\n10.1016\u002FS0262-8856(00)00041-X\ncohen, 1991, automated inspection of textile fabrics using textural models, IEEE Trans Pattern Anal Machine Intelll, 803, 10.1109\u002F34.85670\n10.1177\u002F004051750007000902\n10.1177\u002F004051759506501109\nlane, 1998, Textile fabric inspection system\n10.1109\u002FICSMC.1998.727546\n10.1109\u002FICSMC.1998.727529\n10.1109\u002F34.3870\n10.1109\u002FTPAMI.1983.4767446\n10.1177\u002F004051759506500301\n10.1109\u002FTENCON.1997.647318\n10.1016\u002FS0169-7552(98)00211-6\nkumar, 2001, Automated defect detection in textured materials\n10.1016\u002F0957-4158(95)00004-O\n10.1007\u002F978-1-4471-3201-1_23\n10.1177\u002F004051759506500101\n10.1117\u002F12.164868\nfukunaga, 1990, Statistical Pattern Recognition 2nd ed\n10.1117\u002F12.171200\n10.1016\u002F0165-1684(86)90095-2\nmahalanobis, 1994, correlation filters for texture recognition and applications to terrain-delimitation in wide-area surveillance, Proc Int Conf Acousti Speech Signal Processing ICASSP 94, 5, 153\n10.1109\u002FICIP.1995.529724\n10.1109\u002F83.753744\n10.1117\u002F12.223998\n10.1109\u002FROBOT.1997.620007\n10.1364\u002FAO.33.002173\ncampbell, 1996, Linear flaw detection in woven textiles using model-based clustering",{"VOID":734},"10.1109\u002FTSMCB.2002.1033176","2025-01-28T14:57:05.825+00:00","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033176\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033176",[739,754],{"id":740,"sortIndex":19,"researcher":18,"roles":741,"affiliations":742,"properties":751,"displayName":753,"givenName":18,"familyName":18},"7a1c2fac-0fb3-4fcc-a044-3f9e640fd38c",[561],[743],{"id":744,"sortIndex":19,"affiliation":745,"properties":18},"5fdb4820-a24d-41a6-9e8b-e55709689703",{"id":744,"createTime":18,"updateTime":18,"relativeEntities":746,"slug":18,"properties":747,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":750,"statistic":18},[],{"title":748},{"VI":749},"Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong, China",[],{"title":752},{"VI":753},"A. Kumar",{"id":755,"sortIndex":75,"researcher":18,"roles":756,"affiliations":757,"properties":766,"displayName":768,"givenName":18,"familyName":18},"ceb38ed6-da25-44d2-9ba4-437f373f39bc",[561],[758],{"id":759,"sortIndex":19,"affiliation":760,"properties":18},"21f53f36-af44-4afb-803c-59ec7c4163c7",{"id":759,"createTime":18,"updateTime":18,"relativeEntities":761,"slug":18,"properties":762,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":765,"statistic":18},[],{"title":763},{"VI":764},"Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China",[],{"title":767},{"VI":768},"G.K.H. Pang",{"url":736,"publisher":770,"properties":778},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":771,"slug":10,"properties":772,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":775,"manageAffiliations":776,"indexDatabases":777,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":773,"title":774},{"VOID":13},{"EN":15},[],[],[],{"issue":779,"pages":780,"volume":782},{"VOID":613},{"VOID":781},"553-570",{"VOID":617},[],{"id":785,"createTime":786,"updateTime":787,"relativeEntities":788,"slug":789,"properties":790,"entityType":45,"verifyStatus":334,"verifyTime":787,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":801,"fullTextUrl":802,"authors":803,"publicationType":109,"publisherRelationship":834,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":848,"openAccess":18,"references":18,"isForceReanalyzing":315},"6cad2707-17e5-4035-9bfe-94b412f021d4","2023-12-24T03:34:12.558+00:00","2025-01-15T21:56:01.827+00:00",[],"Complexity-reduction-for-large-image-processing",{"abstract":791,"title":793,"keywords":795,"references":797,"doi":799},{"EN":792},"We present a method for sampling feature vectors in large (e.g., 2000 \u002Fspl times\u002F 5000 \u002Fspl times\u002F 16 bit) images that finds subsets of pixel locations which represent c \"regions\" in the image. Samples are accepted by the chi-square (\u002Fspl chi\u002F\u002Fsup 2\u002F) or divergence hypothesis test. A framework that captures the idea of efficient extension of image processing algorithms from the samples to the rest of the population is given. Computationally expensive (in time and\u002For space) image operators (e.g., neural networks (NNs) or clustering models) are trained on the sample, and then extended noniteratively to the rest of the population. We illustrate the general method using fuzzy c-means (FCM) clustering to segment Indian satellite images. On average, the new method can achieve about 99% accuracy (relative to running the literal algorithm) using roughly 24% of the image for training. This amounts to an average savings of 76% in CPU time. We also compare our method to its closest relative in the group of schemes used to accelerate FCM: our method averages a speedup of about 4.2, whereas the multistage random sampling approach achieves an average acceleration of 1.63.",{"EN":794},"Complexity reduction for \"large image\" processing",{"EN":796},"Image processing,Image sampling,Clustering algorithms,Acceleration,Pixel,Testing,Computer networks,Neural networks,Image segmentation,Satellites",{"VOID":798},"10.1007\u002F978-3-642-88087-2\n1993, Interactive Data Language (IDL) Ver 3 1 Users Guide\nde la paz, 1986, approximate fuzzy \u003Cformula>\u003Ctex>$c$\u003C\u002Ftex>\u003C\u002Fformula>-means (afcm) cluster analysis of medical magnetic resonance image (mri) data—a system for medical research and education, IEEE Transactions on Geoscience and Remote Sensing, ge 25, 815\nmorrison, 1994, an introduction to segmentation of magnetic resonance images, Aust Comput J, 26, 90\n10.1109\u002FTPAMI.1986.4767778\n10.1109\u002FTGRS.1986.289598\n10.1016\u002F0031-3203(94)90118-X\n10.1109\u002FNAFIPS.2001.944766\numa shankar, 1994, ffcm: an effective approach for large data sets, Proc 3rd Int Conf Fuzzy Logic Neural Nets and Soft Computing IIZUKA, 331\ncheng, 1995, fast clustering with application to fuzzy rule generation, Proceedings of the IEEE International Conference on Fuzzy Systems, 2289\n10.1145\u002F223784.223812\n10.1016\u002FS0306-4379(01)00008-4\nbezdek, 2002, Some Notes on Alternating Optimization in Advances in Soft Computing AFSS, 289\ndomingos, 2001, a general method for scaling up machine learning algorithms and its application to clustering, Proc 18th Int Conf Machine Learning, 106\n10.1080\u002F01621459.1963.10500830\nharalick, 1992, Computer and Robot Vision, 1\ngonzalez, 1992, Digital Image Processing\nkendall, 1979, The Advanced Theory of Statistics Vol 2 Inference and Relationship\ngibbons, 1992, Nonparametric Statistical Inference\ncochran, 1977, Sampling Techniques\nkullback, 1959, Information Theory and Statistics\n10.1007\u002F978-1-4757-0450-1\n10.1111\u002Fj.1365-2818.1990.tb02958.x\nchen, 2000, intelligent methodology for sensing, modeling and control of pulsed gtaw: part ii—butt joint welding, Weld Res Supplement, 164\n10.1109\u002F72.159057\nzhang, 1995, birch: an efficient data clustering method for very large databases, Proc ACM SIGMOD, 103\nfayyad, 1995, from massive data sets to science catalogs: applications and challenges, Proc Workshop Massive Data Sets\n10.1002\u002Fjmri.1880050520\n10.1109\u002F2.781633\nbradley, 1998, scaling clustering algorithms to large databases, Proc 4th Int Conf Knowledge Discovery and Data Mining, 9\nchen, 2000, intelligent methodology for sensing, modeling, and control of pulsed gtaw: part i—bead-on-plate welding, Weld Res Supplement, 151\n10.1109\u002FICDE.1999.754966\nbowyer, 1996, the digital database for screening mammography, Proc 3rd Int Workshop Digital Mammography 58\nfisher, 1954, Statistical Methods for Research Workers\n10.1109\u002F91.660808\nrosenfeld, 1982, Digital Picture Processing\n10.1007\u002Fb106267\nhathaway, 0, convergence of alternating optimization, Comput Optim Appl\n10.1109\u002FFUZZY.1994.343855\njain, 1996, image segmentation by clustering, Advances in Image Understanding, 65\n10.1109\u002F91.917113",{"VOID":800},"10.1109\u002FTSMCB.2002.1033179","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033179\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033179",[804,819],{"id":805,"sortIndex":19,"researcher":18,"roles":806,"affiliations":807,"properties":816,"displayName":818,"givenName":18,"familyName":18},"68eaeb4e-287c-4678-895f-a629dcfe065e",[561],[808],{"id":809,"sortIndex":19,"affiliation":810,"properties":18},"290ed5c3-b868-4ec5-a037-c7efacb708e0",{"id":809,"createTime":18,"updateTime":18,"relativeEntities":811,"slug":18,"properties":812,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":815,"statistic":18},[],{"title":813},{"VI":814},"Electronics and Communication Sciences Unit, Indian Statistical Institute, Calcutta, India",[],{"title":817},{"VI":818},"N.R. Pal",{"id":820,"sortIndex":75,"researcher":18,"roles":821,"affiliations":822,"properties":831,"displayName":833,"givenName":18,"familyName":18},"0787228f-1495-4a24-98ae-892e2e56f85a",[561],[823],{"id":824,"sortIndex":19,"affiliation":825,"properties":18},"18886b9a-1984-4917-a5a1-f53c75fcdef1",{"id":824,"createTime":18,"updateTime":18,"relativeEntities":826,"slug":18,"properties":827,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":830,"statistic":18},[],{"title":828},{"VI":829},"Department of Computer Science, University of West Florida, Pensacola, FL, USA",[],{"title":832},{"VI":833},"J.C. Bezdek",{"url":801,"publisher":835,"properties":843},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":836,"slug":10,"properties":837,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":840,"manageAffiliations":841,"indexDatabases":842,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":838,"title":839},{"VOID":13},{"EN":15},[],[],[],{"issue":844,"pages":845,"volume":847},{"VOID":613},{"VOID":846},"598-611",{"VOID":617},[],{"id":850,"createTime":851,"updateTime":852,"relativeEntities":853,"slug":854,"properties":855,"entityType":45,"verifyStatus":334,"verifyTime":852,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":866,"fullTextUrl":867,"authors":868,"publicationType":109,"publisherRelationship":912,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":926,"openAccess":18,"references":18,"isForceReanalyzing":315},"811f0f98-4c8c-410e-99f5-37532c2ac6fe","2023-12-24T03:35:30.945+00:00","2025-01-12T12:01:57.743+00:00",[],"Generating-learning-sequences-for-decision-makers-through-data-mining-and-competence-set-expansion",{"abstract":856,"title":858,"keywords":860,"references":862,"doi":864},{"EN":857},"For each decision problem, there is a competence set, proposed by Yu (1990), consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences for acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method (Feng and Yu (1998)). From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.",{"EN":859},"Generating learning sequences for decision makers through data mining and competence set expansion",{"EN":861},"Data mining,Relational databases,Costs,Decision making,Fuzzy sets,Pattern analysis,Time measurement,Information management,Technology management,Mathematics",{"VOID":863},"10.1109\u002F3477.604117\nhan, 2001, Data Mining Concepts and Techniques\n10.1109\u002F91.413232\n10.1109\u002F3477.790443\n10.1016\u002FS0360-8352(02)00136-5\n10.1109\u002F21.199466\n10.1109\u002F91.273127\n10.1007\u002F978-1-4757-0450-1\nagrawal, 1995, fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, 307\n10.1007\u002FBF02196594\nhwang, 2001, multistages optimal expansion of competence sets in fuzzy environment, Int J Fuzzy Syst, 3, 486\n10.1016\u002F0165-4896(90)90005-R\n10.1023\u002FA:1021755117744\n10.1016\u002FS0950-7051(02)00079-5\n10.1016\u002F0020-0255(75)90036-5\n10.1016\u002FS0019-9958(65)90241-X\n10.1145\u002F345124.345167\nberry, 1997, Data Mining Techniques For Marketing Sales and Customer Support\n10.1007\u002F978-94-015-7949-0\nhu, 2001, discovering fuzzy concepts for expanding competence set, Proc 2nd Int Symp Advanced Intelligent Systems, 396\nadriaans, 1996, Data Mining\npedrycz, 1998, An Introduction to Fuzzy Sets Analysis and Design, 10.7551\u002Fmitpress\u002F3926.001.0001\n10.1007\u002F978-3-642-61295-4\n10.1016\u002FS0377-2217(98)00397-X",{"VOID":865},"10.1109\u002FTSMCB.2002.1033188","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033188\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033188",[869,884,897],{"id":870,"sortIndex":19,"researcher":18,"roles":871,"affiliations":872,"properties":881,"displayName":883,"givenName":18,"familyName":18},"b9272453-5acc-4eb7-967a-5fb6a5fc6cdf",[561],[873],{"id":874,"sortIndex":19,"affiliation":875,"properties":18},"8ee47d62-8701-49f2-98bc-bd0dbd5eaad8",{"id":874,"createTime":18,"updateTime":18,"relativeEntities":876,"slug":18,"properties":877,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":880,"statistic":18},[],{"title":878},{"VI":879},"Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan",[],{"title":882},{"VI":883},"Yi-Chung Hu",{"id":885,"sortIndex":75,"researcher":18,"roles":886,"affiliations":887,"properties":894,"displayName":896,"givenName":18,"familyName":18},"03ef980d-fa36-4aca-8ed1-bddec33e0e72",[561],[888],{"id":874,"sortIndex":19,"affiliation":889,"properties":18},{"id":874,"createTime":18,"updateTime":18,"relativeEntities":890,"slug":18,"properties":891,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":893,"statistic":18},[],{"title":892},{"VI":879},[],{"title":895},{"VI":896},"Ruey-Shun Chen",{"id":898,"sortIndex":87,"researcher":18,"roles":899,"affiliations":900,"properties":909,"displayName":911,"givenName":18,"familyName":18},"c830e53b-74a4-43e1-be56-1e511371c5b3",[561],[901],{"id":902,"sortIndex":19,"affiliation":903,"properties":18},"594de789-4bd0-4434-a3e9-24374113163f",{"id":902,"createTime":18,"updateTime":18,"relativeEntities":904,"slug":18,"properties":905,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":908,"statistic":18},[],{"title":906},{"VI":907},"Institute of Management of Technology, National Chiao Tung University, Hsinchu, Taiwan",[],{"title":910},{"VI":911},"Gwo-Hshiung Tzeng",{"url":866,"publisher":913,"properties":921},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":914,"slug":10,"properties":915,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":918,"manageAffiliations":919,"indexDatabases":920,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":916,"title":917},{"VOID":13},{"EN":15},[],[],[],{"issue":922,"pages":923,"volume":925},{"VOID":613},{"VOID":924},"679-686",{"VOID":617},[],{"id":928,"createTime":929,"updateTime":930,"relativeEntities":931,"slug":932,"properties":933,"entityType":45,"verifyStatus":334,"verifyTime":944,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":945,"fullTextUrl":946,"authors":947,"publicationType":109,"publisherRelationship":976,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":990,"openAccess":18,"references":18,"isForceReanalyzing":315},"16821045-16c5-4e24-99ea-e64866944427","2023-12-24T03:34:54.816+00:00","2025-01-12T08:14:22.715+00:00",[],"Embedding-fuzzy-mechanisms-and-knowledge-in-box-type-reinforcement-learning-controllers",{"abstract":934,"title":936,"keywords":938,"references":940,"doi":942},{"EN":935},"In this paper, we report our study on embedding fuzzy mechanisms and knowledge into box-type reinforcement learning controllers. One previous approach for incorporating fuzzy mechanisms can only achieve one successful run out of nine tests compared to eight successful runs in a nonfuzzy learning control scheme. After analysis, the credit assignment problem and the weighting domination problem are identified. Furthermore, the use of fuzzy mechanisms in temporal difference seems to play a negative factor. Modifications to overcome those problems are proposed. Furthermore, several remedies are employed in that approach. The effects of those remedies applied to our learning scheme are presented and possible variations are also studied. Finally, the issue of incorporating knowledge into reinforcement learning systems is studied. From our simulations, it is concluded that the use of knowledge for the control network can provide good learning results, but the use of knowledge for the evaluation network alone seems unable to provide any significant advantages. Furthermore, we also employ Makarovic's (1988) rules as the knowledge for the initial setting of the control network. In our study, the rules are separated into four groups to avoid the ordering problem.",{"EN":937},"Embedding fuzzy mechanisms and knowledge in box-type reinforcement learning controllers",{"EN":939},"Fuzzy control,Supervised learning,Control systems,Unsupervised learning,System performance,Testing,Learning systems,Control system synthesis,Neural networks,Fuzzy systems",{"VOID":941},"10.1109\u002F91.273126\n10.1109\u002F72.159061\n10.1109\u002FICSMC.1999.816639\nschoknecht, 1999, using reinforcement learning for engine control, Proceedings of 9th International Conference on Artificial Neural Networks ICANN 99, 1, 329, 10.1049\u002Fcp:19991130\n10.1109\u002FIJCNN.1999.833414\n10.1109\u002FICIT.2000.854247\n10.1109\u002F3477.836376\n10.1007\u002FBF00115009\nhsieh, 1997, On the study of embedding fuzzy concept and prior knowledge in reinforcement learning\nkim, 1995, Analysis and study of neural-networks-based reinforcement learning\n10.1109\u002FTAC.1965.1098193\n10.1109\u002FJRPROC.1961.287775\n10.1109\u002FTSMC.1983.6313077\n10.1147\u002Frd.33.0210\n10.1002\u002Fint.4550060105\n10.1109\u002F37.24809\nlin, 1996, Neural Fuzzy System A Neuro-Fuzzy Synergism to Intelligent Systems\nkokar, 1992, learning control methods, needs, and architectures, An Introduction to Intelligent and Autonomous Control\n10.1080\u002F02533839.2001.9670633\n10.1109\u002FTSMC.1985.6313399\nyager, 1994, Essentials of Fuzzy Modeling and Control\n10.1109\u002FFUZZY.1992.258745\n10.1109\u002F21.97472\n10.1109\u002FTAC.1997.633847\nmakarovic, 1988, A qualitative way of solving the pole balancing problem",{"VOID":943},"10.1109\u002FTSMCB.2002.1033183","2025-01-12T08:14:22.714+00:00","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033183\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033183",[948,963],{"id":949,"sortIndex":19,"researcher":18,"roles":950,"affiliations":951,"properties":960,"displayName":962,"givenName":18,"familyName":18},"a7a0f814-29eb-484d-a8fb-c52e7f20f18e",[561],[952],{"id":953,"sortIndex":19,"affiliation":954,"properties":18},"210aabd1-e38d-4753-9537-0056528556c6",{"id":953,"createTime":18,"updateTime":18,"relativeEntities":955,"slug":18,"properties":956,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":959,"statistic":18},[],{"title":957},{"VI":958},"National Taiwan University of Science and Technology, Taipei, Taiwan",[],{"title":961},{"VI":962},"Shun-Feng Su",{"id":964,"sortIndex":75,"researcher":18,"roles":965,"affiliations":966,"properties":973,"displayName":975,"givenName":18,"familyName":18},"28aef2d0-5103-4acb-a5b8-d325e42305ff",[561],[967],{"id":953,"sortIndex":19,"affiliation":968,"properties":18},{"id":953,"createTime":18,"updateTime":18,"relativeEntities":969,"slug":18,"properties":970,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":972,"statistic":18},[],{"title":971},{"VI":958},[],{"title":974},{"VI":975},"Sheng-Hsiung Hsieh",{"url":945,"publisher":977,"properties":985},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":978,"slug":10,"properties":979,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":982,"manageAffiliations":983,"indexDatabases":984,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":980,"title":981},{"VOID":13},{"EN":15},[],[],[],{"issue":986,"pages":987,"volume":989},{"VOID":613},{"VOID":988},"645-653",{"VOID":617},[],{"id":992,"createTime":993,"updateTime":994,"relativeEntities":995,"slug":996,"properties":997,"entityType":45,"verifyStatus":17,"verifyTime":994,"verifyNote":47,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1008,"fullTextUrl":1009,"authors":1010,"publicationType":109,"publisherRelationship":1048,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1062,"openAccess":18,"references":18,"isForceReanalyzing":315},"88d1659e-6304-40f6-9736-2fadd365d1e7","2023-12-24T03:35:06.879+00:00","2025-01-07T19:12:33.622+00:00",[],"A-merge-based-condensing-strategy-for-multiple-prototype-classifiers",{"abstract":998,"title":1000,"keywords":1002,"references":1004,"doi":1006},{"EN":999},"A class-conditional hierarchical clustering framework has been used to generalize and improve previously proposed condensing schemes to obtain multiple prototype classifiers. The proposed method conveniently uses geometric properties and clusters to efficiently obtain reduced sets of prototypes that accurately represent the data while significantly keeping its discriminating power. The benefits of the proposed approach are empirically assessed with regard to other previously proposed algorithms which are similar in their foundations. Other well-known multiple prototype classifiers have also been taken into account in the comparison.",{"EN":1001},"A merge-based condensing strategy for multiple prototype classifiers",{"EN":1003},"Prototypes,Clustering algorithms,Nearest neighbor searches,Neural networks,Adaptive algorithm",{"VOID":1005},"kohonen, 1995, Self-Organizing Maps, 10.1007\u002F978-3-642-97610-0\n10.1016\u002F0031-3203(93)90040-4\n10.1016\u002F0167-8655(94)00070-J\n10.1093\u002Fcomjnl\u002F26.4.354\n10.1109\u002FIVELEC.2009.5193407\n10.1109\u002FICNN.1988.23829\nking, 1992, Statlog Databases\n10.1016\u002FS0167-8655(97)00035-4\n10.1109\u002FTSMC.1972.4309137\n1997, LVQ PAK The learning vector quantization package\n10.1109\u002F21.278999\n10.1109\u002FTIT.1968.1054155\nchang, 1974, finding prototypes for nearest neighbor classifiers, IEEE Trans Computes, c 23, 1179, 10.1109\u002FT-C.1974.223827\n10.1109\u002F5326.661099\n10.1109\u002F5326.661091\n10.1109\u002F5.58325\ndevijver, 1982, Pattern Recognition a Statistical Approach\nmollineda, 2001, Hierarchical agglomerative clustering for prototype selection and distance-based classification use in cyclic strings\ndasarathy, 1991, Nearest Neighbor (NN) Norms NN Pattern Classification Techniques\nwebb, 1999, Statistical Pattern Recognition",{"VOID":1007},"10.1109\u002FTSMCB.2002.1033185","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033185\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033185",[1011,1026,1041],{"id":1012,"sortIndex":19,"researcher":18,"roles":1013,"affiliations":1014,"properties":1023,"displayName":1025,"givenName":18,"familyName":18},"547325fc-903b-4c8c-a013-c08ba0c57ea4",[561],[1015],{"id":1016,"sortIndex":19,"affiliation":1017,"properties":18},"fc2b6ccb-4ecd-4ac3-a643-f2829cef7246",{"id":1016,"createTime":18,"updateTime":18,"relativeEntities":1018,"slug":18,"properties":1019,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1022,"statistic":18},[],{"title":1020},{"VI":1021},"Institut Tecnològic dInformàtica, Universitat Politécnica de Valéncia, Valencia, Spain",[],{"title":1024},{"VI":1025},"R.A. Mollineda",{"id":1027,"sortIndex":75,"researcher":18,"roles":1028,"affiliations":1029,"properties":1038,"displayName":1040,"givenName":18,"familyName":18},"754ff62f-0149-46b1-b6ba-9cfcc443faa3",[561],[1030],{"id":1031,"sortIndex":19,"affiliation":1032,"properties":18},"0f361b62-b141-4768-8211-73392f709553",{"id":1031,"createTime":18,"updateTime":18,"relativeEntities":1033,"slug":18,"properties":1034,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1037,"statistic":18},[],{"title":1035},{"VI":1036},"Departamento dInformática, Universitat de València, Burjassot, Spain",[],{"title":1039},{"VI":1040},"F.J. Ferri",{"id":1042,"sortIndex":87,"researcher":18,"roles":1043,"affiliations":1044,"properties":1045,"displayName":1047,"givenName":18,"familyName":18},"de4e6003-eb0d-40f4-b905-124cbf29c5f7",[561],[],{"title":1046},{"VI":1047},"E. Vidal",{"url":1008,"publisher":1049,"properties":1057},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1050,"slug":10,"properties":1051,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1054,"manageAffiliations":1055,"indexDatabases":1056,"url":18,"thumbnailPath":18,"statistic":18,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1052,"title":1053},{"VOID":13},{"EN":15},[],[],[],{"issue":1058,"pages":1059,"volume":1061},{"VOID":613},{"VOID":1060},"662-668",{"VOID":617},[],{"id":1064,"createTime":1065,"updateTime":1066,"relativeEntities":1067,"slug":1068,"properties":1069,"entityType":45,"verifyStatus":334,"verifyTime":1066,"verifyNote":336,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1080,"fullTextUrl":1081,"authors":1082,"publicationType":109,"publisherRelationship":1148,"citationCount":18,"citationInfo":18,"publishDate":618,"publishYear":619,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":1162,"openAccess":18,"references":18,"isForceReanalyzing":315},"c59db6bf-3d55-405b-9358-a309e2cbc9dd","2023-12-24T03:34:03.939+00:00","2025-01-01T02:38:02.348+00:00",[],"Adaptive-hybrid-intelligent-control-for-uncertain-nonlinear-dynamical-systems",{"abstract":1070,"title":1072,"keywords":1074,"references":1076,"doi":1078},{"EN":1071},"A new hybrid direct\u002Findirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct\u002Findirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.",{"EN":1073},"Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems",{"EN":1075},"Programmable control,Adaptive control,Intelligent control,Nonlinear dynamical systems,Fuzzy neural networks,Fuzzy control,Control systems,Force control,Nonlinear control systems,Stability",{"VOID":1077},"ma, 2000, output tracking and regulation of nonlinear system based on takgi–sugeno fuzzy model, IEEE Trans Syst Man Cybernetics, 30, 47, 10.1109\u002F3477.826946\n10.1109\u002F72.809085\n10.1007\u002F978-3-642-31362-2_46\n10.1109\u002F21.376496\n10.1109\u002F70.795786\n10.1109\u002F21.370193\n10.1109\u002FTCS.1986.1085869\n10.1007\u002F978-3-662-02581-9\n10.1109\u002FTSMC.1985.6313399\n10.1109\u002F91.755401\n10.1109\u002F91.481843\n10.1109\u002F9.186310\n10.1109\u002F21.478446\n10.1109\u002F21.278990\nwang, 1994, Adaptive Fuzzy Systems and Control Design and Stability Analysis\n10.1109\u002F91.227383\n10.1109\u002F9.186309\n10.1109\u002F91.531775\n10.1109\u002F9.40741\n10.1109\u002F91.797976\n10.1109\u002FFUZZY.2000.839134\n10.1109\u002F91.755395\n10.1109\u002F91.784199\nslotine, 1991, Applied nonlinear control\nchen, 1999, Linear System Theory and Design\nogata, 1987, Discrete-Time Control Systems",{"VOID":1079},"10.1109\u002FTSMCB.2002.1033178","https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F1033178\u002F","https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=1033178",[1083,1098,1120,1135],{"id":1084,"sortIndex":19,"researcher":18,"roles":1085,"affiliations":1086,"properties":1095,"displayName":1097,"givenName":18,"familyName":18},"92f304b5-034f-41ef-b57a-331822af0ac8",[561],[1087],{"id":1088,"sortIndex":19,"affiliation":1089,"properties":18},"8838302e-379b-4f9d-8ffe-b7c4e4c62ec6",{"id":1088,"createTime":18,"updateTime":18,"relativeEntities":1090,"slug":18,"properties":1091,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1094,"statistic":18},[],{"title":1092},{"VI":1093},"School of Microelectronic Engineering, Griffith University, Brisbane, Australia",[],{"title":1096},{"VI":1097},"Chi-Hsu Wang",{"id":1099,"sortIndex":75,"researcher":18,"roles":1100,"affiliations":1101,"properties":1117,"displayName":1119,"givenName":18,"familyName":18},"17568c32-42fa-499d-8192-7059f9d5690f",[561],[1102,1108],{"id":1088,"sortIndex":19,"affiliation":1103,"properties":18},{"id":1088,"createTime":18,"updateTime":18,"relativeEntities":1104,"slug":18,"properties":1105,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1107,"statistic":18},[],{"title":1106},{"VI":1093},[],{"id":1109,"sortIndex":75,"affiliation":1110,"properties":1116},"8928668b-257b-4a85-a61f-d65995b05a2c",{"id":1109,"createTime":18,"updateTime":18,"relativeEntities":1111,"slug":18,"properties":1112,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1115,"statistic":18},[],{"title":1113},{"VI":1114},"Department of Electronic Engineering, Feng Chia University FCU, Taichung, Taiwan",[],{},{"title":1118},{"VI":1119},"Tsung-Chih Lin",{"id":1121,"sortIndex":87,"researcher":18,"roles":1122,"affiliations":1123,"properties":1132,"displayName":1134,"givenName":18,"familyName":18},"29eb3a84-5ad3-46a8-bb67-ad47bc1a4813",[561],[1124],{"id":1125,"sortIndex":19,"affiliation":1126,"properties":18},"612eb2aa-6bb9-4975-bb9a-879f8c52b787",{"id":1125,"createTime":18,"updateTime":18,"relativeEntities":1127,"slug":18,"properties":1128,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1131,"statistic":18},[],{"title":1129},{"VI":1130},"Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan",[],{"title":1133},{"VI":1134},"Tsu-Tian Lee",{"id":1136,"sortIndex":99,"researcher":18,"roles":1137,"affiliations":1138,"properties":1145,"displayName":1147,"givenName":18,"familyName":18},"8412d686-2ff4-4d53-801e-e0d6eb19fc05",[561],[1139],{"id":1088,"sortIndex":19,"affiliation":1140,"properties":18},{"id":1088,"createTime":18,"updateTime":18,"relativeEntities":1141,"slug":18,"properties":1142,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1144,"statistic":18},[],{"title":1143},{"VI":1093},[],{"title":1146},{"VI":1147},"Han-Leih 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