Analyzing and forecasting the global CO2 concentration – a collaborative fuzzy–neural agent network approach

Journal of Applied Research and Technology - Tập 13 - Trang 364-373 - 2015
T. Chen1
1Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City, Taiwan

Tài liệu tham khảo

Ahmadaali, 2013, Estimation of virtual water using support vector machine, K-nearest neighbour, and radial basis function neural network models, Int. J. Agronomy Plant Production, 4, 2926 Babaei, 2013, Dimension Estimation of Rectangular Cracks Using Impedance Changes of the Eddy Current Probe with a Neural Network, Journal of Applied Research and Technology, 11, 397, 10.1016/S1665-6423(13)71549-9 Bhattacharya, 2007, Soft-sensing of level of satisfaction in TOC product-mix decision heuristic using robust fuzzy-LP, European Journal of Operational Research, 177, 55, 10.1016/j.ejor.2005.11.017 Chen, 2008, A SOM-FBPN-ensemble approach with error feedback to adjust classification for wafer-lot completion time prediction, The International Journal of Advanced Manufacturing Technology, 37, 782, 10.1007/s00170-007-1007-y Chen, 2012, A collaborative fuzzy-neural system for global CO2 concentration forecasting, International Journal of Innovative Computing, Information and Control, 8, 7679 Chen, 2011, A fuzzy-neural approach for global CO2 concentration forecasting, Intelligent Data Analysis, 15, 763, 10.3233/IDA-2011-0494 Chen, 2012, Long-term load forecasting by a collaborative fuzzy-neural approach, International Journal of Electrical Power & Energy Systems, 43, 454, 10.1016/j.ijepes.2012.05.072 Chen, 2014, An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting, IEEE Transactions on Fuzzy Systems, 22, 201, 10.1109/TFUZZ.2013.2250290 CO2Now.org (2013). Accelerating rise of atmospheric CO2. Retrieved from: http://co2now.org/. Earth System Research Laboratory Global Monitoring Division (2014). Trends in atmospheric carbon dioxide. Retrieved from: http://www.esrl.noaa.gov/gmd/ccgg/trends/. Endo, T., Banno, A., & Tamura, Y. (2008). Research into sensor networks and Web APIs-Urban navigation systems utilising sensor network data. In: 5th International Conference on Networked Sensing Systems, 2008. INSS 2008 (pp. 166-169). IEEE. Eraslan, E. (2009). The estimation of product standard time by artificial neural networks in the molding industry. Mathematical Problems in Engineering, 2009, article ID 527452. Firoze, 2013, Bangla user adaptive word Speech recognition: approaches and comparisons, International Journal of Fuzzy System Applications (IJFSA), 3, 1, 10.4018/ijfsa.2013070101 Hong, 2011, Electric load forecasting by seasonal recurrent SVR with chaotic artificial bee colony algorithm, Energy, 36, 5568, 10.1016/j.energy.2011.07.015 Lee, 2004, An intelligent fuzzy agent for meeting scheduling decision support system, Fuzzy Sets and Systems, 142, 467, 10.1016/S0165-0114(03)00201-X López-Juárez, 2013, Using Object's Contour, Form and Depth to Embed Recognition Capability into Industrial Robots, Journal of Applied Research and Technology, 11, 5, 10.1016/S1665-6423(13)71511-6 Lu, 2009, A real-time decision-making of maintenance using fuzzy agent, Expert Systems with Applications, 36, 2691, 10.1016/j.eswa.2008.01.087 Morreale, P.A. (2007, May). Wireless sensor network applications in urban telehealth. In: 21st International Conference on Advanced Information Networking and Applications Workshops, 2007, AINAW’07 (Vol. 2, pp. 810-814). IEEE. National Assessment Synthesis Team, 2000 Pedrycz, 2008, Collaborative architectures of fuzzy modeling, Lecture Notes in Computer Science, 5050, 117, 10.1007/978-3-540-68860-0_6 Peidro, 2011, Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach, Applied Soft Computing, 11, 2656, 10.1016/j.asoc.2010.10.014 Uchimura, Y., Nasu, T., & Takahashi, M. (2007, September). Time synchronized wireless sensor network for vibration measurement. In: SICE, 2007 Annual Conference (pp. 2940-2945). IEEE. Wang, Q., Zhang, T., & Pettersson, S. (2008). An effort to understand the optimal routing performance in wireless sensor network. In: 22nd International Conference on Advanced Information Networking and Applications, 2008. AINA 2008 (pp. 279-286). IEEE. Wrather, 1982, Probability dominance in random outcomes, Journal of Optimization Theory and Applications, 36, 315, 10.1007/BF00934350 Yan, 2009, Research on opportunistic cooperation transmission and its performance in wireless sensor network, Journal of Electronics and Information Technology, 31, 215 Zarandi, 2008, A fuzzy agent-based model for reduction of bullwhip effect in supply chain systems, Expert Systems with Applications, 34, 1680, 10.1016/j.eswa.2007.01.031