Abstract
The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional type single input rule modules connected fuzzy inference method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs method can not realize XOR (Exclusive OR). In this paper, we propose a “neural network-type SIRMs method” which unites the neural network and SIRMs method, and show that this method can realize XOR. Further, a learning algorithm of the proposed SIRMs method is derived by steepest descent method, and is shown to be superior to the conventional SIRMs method and neural network by applying to identification of nonlinear functions.
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© 2009 Springer-Verlag Berlin Heidelberg
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Seki, H., Watanabe, S., Ishii, H., Mizumoto, M. (2009). Realization of XOR by SIRMs Connected Fuzzy Inference Method. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_26
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DOI: https://doi.org/10.1007/978-3-642-02568-6_26
Publisher Name: Springer, Berlin, Heidelberg
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