Abstract
Recently, fuzzy reasoning has been used in many fields and places. In order to apply the reasoning to the various fields, the tuning and optimizing method of the fuzzy reasoning is the key issue. Some self-tuning methods have been proposed so far. However, these conventional self-tuning methods do not have sufficient capability of learning. In this paper, we propose new unsupervlsed/supervised self-tuning methods for fuzzy reasoning, which consists of membership functions expressed by the radial basis function with an insensitive region. Learning is carried out by a genetic algorithm. The gradient decent method is also used for tuning the shapes and location of membership function and consequent parts in case of supervised learning. The effectiveness of the proposed methods is shown by some numerical examples.
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© 1995 Springer-Verlag Berlin Heidelberg
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Shimojima, K., Hasegawa, Y., Fukuda, T. (1995). Unsupervised/supervised learning for RBF-fuzzy system. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_10
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DOI: https://doi.org/10.1007/3-540-60607-6_10
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