Commenced in January 2007
Paper Count: 30075
Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules
Abstract:Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1112031Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1149
 B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, Englewood Cliffs, NJ, 1992.
 C. Lin, and C. Lee, Neural Fuzzy Systems, Prentice Hall, PTR, 1996.
 M.M. Gupta, L. Jin, and N. Homma, Static and Dynamic Neural Networks, IEEE Press, 2003.
 J. Casillas, O. Cordon, F. Herrera, and L. Magdalena, “Accuracy Improvements in Linguistic Fuzzy Modeling,” Studies in Fuzziness and Soft Computing, Vol.129, Springer, 2003.
 B. Liu, Theory and Practice of Uncertain Programming, Studies in Fuzziness and Soft Computing, Vol.239, Springer, 2009.
 O. Cordon, “A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems,” Journal of Approximate Reasoning, 52, pp.894-913, 2011.
 K. Kishida, H. Miyajima, M. Maeda, and S. Murashima, “A Self-tuning Method of Fuzzy Modeling using Vector Quantization,” Proceedings of FUZZ-IEEE’97, pp.397-402, 1997.
 W. Pedrycz, and H. Izakian, “Cluster-Centric Fuzzy Modeling,” IEEE Trans. on Fuzzy Systems, Vol.22, Issue 6, pp. 1585-1597, 2014.
 S. Fukumoto, and H. Miyajima, “Learning Algorithms with Regularization Criteria for Fuzzy Reasoning Model,” Journal of Innovative Computing, Information and Control, vol.1, no.1, pp.249-263, 2006.
 N. Yubazaki, J. Yi, and K. Hirota, “SIRMS (Single Input Rule Modules) Connected Fuzzy Inference Model,” J. Advanced Computational Intelligence, 1, 1, pp.23-30, 1997.
 N. Shigei, H. Miyajima, and S. Nagamine, “A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules,” Proc. of Int. Symp. on Neural Networks: Advances in Neural Networks - Part II, pp.118-126, 2009.
 S. Miike, H. Miyajima, N. Shigei, and K. Noo, “Fuzzy Reasoning Model with Deletion of Rules Consisting of Small-Number-of-Input Rule Modules,” Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, pp.621-629, 2010 (in Japanese).
 H. Miyajima, N. Shigei, and H. Miyajima, “An Application of Fuzzy Inference System Composed of Double-Input Rule Modules to Control Problems,” Proceedings of the International MultiConference of Engineers and Computer Scientists 2014, Vol I, pp.23-28, 2014.
 H. Miyajima, N. Shigei, and H. Miyajima, “Some Properties on Fuzzy Inference Systems Composed of Small Number of Input Rule Modules,” Advances in Fuzzy Sets and Systems, Vol.20, pp.155-175, 2015.
 G.E.P. Box, G.M. Jenkins, Time series analysis, forecasting and control,second ed., Holden Day, San Francisco, CA, 1970.
 E. Kim, M. Park, S. Ji, and M. Park, “A new approach to fuzzy modeling,” IEEE Trans. Fuzzy Systems, vol. 5, no. 3, pp. 328-337, 1997.
 C. Li, J. Zhou, B. Fu, P. Kou, and J. Xiao, “T-S fuzzy model identification with a gravitational search-based hyperplane clustering algorithm,” IEEE Trans. Fuzzy Syst., vol.20, no.2, pp. 305-317, 2012.