Integration of Learning Algorithm on Fuzzy Min-Max Neural Networks

doi:10.1007/s12204-017-1894-5

Abstract

Abstract: An integrated fuzzy min-max neural network (IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering, pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.

Key words: fuzzy min-max neural network (FMMNN)| supervised and unsupervised learning| clustering and classification| learning algorithm| similarity

摘要： An integrated fuzzy min-max neural network (IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering, pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.

关键词: fuzzy min-max neural network (FMMNN)| supervised and unsupervised learning| clustering and classification| learning algorithm| similarity

CLC Number:

TP 312

HU Jing* (胡静), LUO Yiyuan (罗宜元). Integration of Learning Algorithm on Fuzzy Min-Max Neural Networks[J]. Journal of Shanghai Jiao Tong University (Science), 2017, 22(6): 733-741.

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