A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules
The automatic construction of fuzzy system with a large number of input variables involves many difficulties such as large time complexities and getting stuck in a shallow local minimum. In order to overcome them, an SIRMs (Single-Input Rule Modules) model has been proposed. However, such a simple model does not always achieve good performance in complex non-linear systems. This paper proposes a fuzzy reasoning model as a generalized SIRMs model, in which each module deals with a small number of input variables. The reasoning output of the model is determined as the weighted sum of all modules, where each weight is the importance degree of a module. Further, in order to construct a simpler model, we introduce a module deletion function according to the importance degree into the proposed system. With the deletion function, we propose a learning algorithm to construct a fuzzy reasoning system consisting of small-number-of-input rule modules (SNIRMs). The conducted numerical simulation shows that the proposed method is superior in terms of accuracy compared to the conventional SIRMs model.
KeywordsFuzzy reasoning model Single-input rule module Small-number-of-input rule module A large number of input variables
Unable to display preview. Download preview PDF.
- 1.Nomura, H., Hayashi, I., Wakami, N.: A Self-Tuning Method of Fuzzy Reasoning by Delta Rule and Its Application to a Moving Obstacle Avoidance. Journal of Japan Society for Fuzzy Theory & Systems 4(2), 379–388 (1992)Google Scholar
- 3.Lin, C., Lee, C.: Neural Fuzzy Systems. Prentice Hall, PTR, Englewood Cliffs (1996)Google Scholar
- 6.Fukumoto, S., Miyajima, H., Kishida, K., Nagasawa, Y.: A Destructive Learning Method of Fuzzy Inference Rules. Proc. of IEEE on Fuzzy Systems, 687–694 (1995)Google Scholar
- 7.Nomura, H., Hayashi, I., Wakami, N.: A Learning Method of Simplified Fuzzy Reasoning by Genetic Algorithm. In: Proc. of the Int. Fuzzy Systems and Intelligent Control Conference, pp. 236–245 (1992)Google Scholar
- 9.Kishida, K., Miyajima, H.: A Learning Method of Fuzzy Inference Rules Using Vector Quantization. In: Proc. of Int. Conf. on Artificial Neural Networks, vol. 2, pp. 827–832 (1998)Google Scholar
- 10.Fukumoto, S., Miyajima, H.: Learning Algorithms with Regularization Criteria for Fuzzy Reasoning Model. Journal of Innovative Computing, Information and Control 1(1), 249–263 (2006)Google Scholar
- 12.Seki, H., Ishii, H., Mizumoto, M.: On the Nonlinear Identification by Functional Type SIRMs Connected Type Fuzzy Reasoning Method. In: Proc. of Int. Conf. on Industrial Eng.–Theory, Applications and Practice, pp. 1441–1446 (2006)Google Scholar