Discriminant analysis by neural network-type SIRMs connected fuzzy inference method

The single input rule modules connected fuzzy inference method (SIRMs method) 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 meth...

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Bibliographic Details
Main Authors: Watanabe, Satoshi, Seki, Hirosato, Ishii, Hiroaki
Format: Conference Proceeding
Language:English
Subjects:
Control systems
Fuzzy neural networks
Fuzzy sets
Fuzzy systems
Iris
Medical diagnosis
Neural networks
Nonlinear control systems
Technology management
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Summary:The single input rule modules connected fuzzy inference method (SIRMs method) 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 methods can not realize XOR (Exclusive OR). Therefore, Seki et al. have proposed a "neural network-type SIRMs method" which unites the neural network and SIRMs method, and shown that this method can realize XOR. In this paper, neuralnetwork-type SIRMs method is shown to be superior to the conventional SIRMs method and neural network by applying to a medical data and Iris data.
ISSN:1935-4576
2378-363X
DOI:10.1109/INDIN.2010.5549455