A new fuzzy controller for stabilization of parallel-type double inverted pendulum system
Fuzzy Sets and Systems
A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control
Fuzzy Sets and Systems - Fuzzy control
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
On the Generalization of Single Input Rule Modules Connected Type Fuzzy Reasoning Method
IEEE Transactions on Fuzzy Systems
On the equivalence of single input type fuzzy inference methods
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.