Pattern analysis for machine olfaction: a review
Tóm tắt
Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.
Từ khóa
#Pattern analysis #Sensor arrays #Feature extraction #Gas detectors #Instruments #Humans #Olfactory #Signal design #Signal processing #Pattern recognitionTài liệu tham khảo
kirkpatrick, 1983, optimization by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671
10.1016/0167-8655(94)90127-9
gutierrez-osuna, 1998, Signal Processing and Pattern Recognition for an Electronic Nose
10.1109/34.824819
10.1016/S0168-1699(96)01307-5
10.1109/T-C.1969.222678
10.1109/TC.1977.1674939
doak, 1992, An Evaluation of Feature Selection Methods and Their Application to Computer Security
devijver, 1982, Pattern Recognition a Statistical Approach
kermani, 1996, On using neural networks and genetic algorithms to optimize the performance of an electronic nose
fukunaga, 1991, Introduction to statistical pattern recognition
10.1016/S0925-4005(97)80335-8
duda, 2000, Pattern Classification
10.1002/1099-128X(200009/12)14:5/6<711::AID-CEM607>3.3.CO;2-W
10.1016/S0925-4005(97)80096-2
gutierrez-osuna, 2000, drift reduction for metal-oxide sensor arrays using canonical correlation regression and partial least squares, Electronic Noses and Olfaction 2000
10.1016/0925-4005(94)01593-7
10.1016/0925-4005(94)87073-X
sundic, 2000, fuzzy logic processing in combined carbon monoxide and methane domestic gas alarms, Electronic Noses and Olfaction 2000
10.1016/S0925-4005(97)80124-4
10.1109/IMTC.1997.610256
bargagna, 2000, fuzzy logic classification of olive oils, Electronic Noses and Olfaction 2000
rumelhart, 1986, learning internal representations by error backpropagation, Parallel Distributed Processing Explorations in the Microstructure of Cognition Vol1 Foundations, 318
le cun, 1998, efficient backprop, Neural Networks Tricks of the Trade, 9, 10.1007/3-540-49430-8_2
10.1016/0893-6080(91)90033-2
sarle, 1995, stopped training and other remedies for overfitting, Proc 27th Symp Interface Comput Sci Statistics, 352
hassibi, 1993, second order derivatives for network pruning: optimal brain surgeon, Advances in Neural Inform Processing Systems, 5, 164
le cun, 1990, optimal brain damage, Advances in Neural Inform Processing Systems, 2, 598
fahlman, 1990, the cascade-correlation learning architecture, Advances in Neural Inform Processing Systems, 2, 524
bishop, 1995, Neural Networks for Pattern Recognition
10.1007/BF02551274
haykin, 1999, Neural Networks A Comprehensive Foundation
10.1007/978-3-662-03315-9
10.1016/S0925-4005(97)80272-9
10.1002/3527601597.ch5
10.1016/0925-4005(94)01572-4
10.1002/1616-8984(199801)3:1<61::AID-SEUP61>3.0.CO;2-7
ikohura, 1994, The Stannic Oxide Gas Sensor
10.1088/0957-0233/2/5/008
dasarathy, 1991, Nearest Neighbor (NN) Norms NN Pattern Classification Techniques
10.1016/0925-4005(90)85002-G
10.1016/S0924-4247(99)00195-8
theodoridis, 1999, Pattern Recognition
10.2307/2289860
10.1007/978-1-4899-3324-9
hall, 1997, feature subset selection: a correlation based filter approach, Proc Intl Conf Neural Inform Processing Intell Inform Syst, 855
10.1016/B978-1-55860-335-6.50023-4
10.1016/S0925-4005(98)00083-5
10.1016/S0003-2670(98)00739-9
10.1002/cem.1180070104
10.1016/0003-2670(86)80028-9
wold, 1975, soft modeling by latent variables; the nonlinear iterative partial least squares approach, Perspectives in Probability and Statistics Papers in Honor of M S Bartlett, 520
10.1017/CBO9780511812651
10.1016/0003-2670(93)80001-2
10.1016/0925-4005(92)80187-3
10.1016/S0003-2670(98)00780-6
10.1016/0925-4005(94)87091-8
10.1016/S0925-4005(99)00386-X
10.1016/S0925-4005(00)00485-8
10.1016/0893-6080(90)90019-H
10.1162/neco.1989.1.2.281
hampshire, 1990, equivalence proofs for multi-layer perceptron classifiers and the bayesian discriminant function, Connectionist Models Proc 1990 Summer School, 159
10.1109/79.180705
10.1109/72.80341
schürmann, 1996, Pattern Classification A Unified View of Statistical and Neural Approaches
gardner, 1999, Electronic Noses Principles and Applications
10.1016/S0303-2647(96)01660-7
10.2307/1269656
10.1109/3477.790446
10.1016/S0925-4005(00)00645-6
10.1088/0957-0233/9/1/016
gardner, 1996, detection of vapors and odours from a multisensor array using pattern recognition: self-organizing adaptive resonance theory, Meas Contr, 29, 172, 10.1177/002029409602900603
10.1006/jcss.1997.1504
grossberg, 1976, adaptive pattern classification and universal recoding: ii. feedback, expectation, olfaction and illusions, Biol Cybern, 23, 187, 10.1007/BF00340335
10.1007/BF00058655
10.1016/S0924-4247(97)80096-9
masters, 1995, Advanced Algorithms for Neural Networks
10.1109/IJCNN.1998.682358
10.1007/978-1-4899-4541-9
10.1016/0925-4005(96)01917-X
hines, 1997, olfactory feature maps from an electronic nose, Meas Contr, 30, 262, 10.1177/002029409703000902
10.1016/S0925-4005(97)80134-7
10.1088/0957-0233/8/11/004
10.1016/S0167-8655(01)00040-X
yea, 1994, the discrimination of many kinds of odour species using fuzzy reasoning and neural networks, Sens Actuators A, 45, 159, 10.1016/0924-4247(94)00831-0
10.1016/S0925-4005(97)00309-2
10.1016/S0303-2647(96)01661-9
10.1016/S0925-4005(99)00288-9
10.1016/S0019-9958(65)90241-X
10.1088/0957-0233/8/2/005
10.1039/a804018d
10.1049/ip-cds:19990670
10.1016/0925-4005(91)80185-M
10.1016/S0925-4005(97)80080-9
10.1016/S0003-2670(97)87788-4
10.1016/S0925-4005(99)00290-7
10.1016/S0925-4005(99)00186-0
10.1039/a804019b
10.1002/3527601597.ch13
10.1016/S0925-4005(97)80283-3
10.1016/0925-4005(92)80203-A
10.1007/s004220050430
10.1016/S0003-2670(99)00784-9
10.1021/ac970427x
10.1109/SENSOR.1995.717323
therrien, 1989, Decision Estimation and Classification An Introduction to Pattern Recognition and Related Topics
10.1016/S0925-2312(01)00455-6
10.1021/ac950671t
10.1016/S0925-4005(97)00240-2
10.1007/BF00317988
10.1126/science.2315702
wilson, 1989, the simulation of large-scale neural networks, Methods in Neuronal Modeling From Synapses to Networks, 291
10.1016/0925-4005(94)01531-L
10.1088/0954-898X/11/1/305
10.1109/BIBE.2001.974433
albrecht, 1994, an intelligent gas sensor system for the identification of hazardous airborne compounds using an array of semiconductor gas sensors and kohonen feature map neural networks, Second International Conference on Intelligent Systems Engineering (Conf Publ No 395), 130, 10.1049/cp:19940614
10.1007/BF00337288
10.1016/0003-2670(94)00085-9
10.1016/0925-4005(94)01595-9
10.1016/S0003-2670(00)82573-8