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Generalized fuzzy inference neural network using a self‐organizing feature map
Author(s) -
Kitajima Hiroshi,
Hagiwara Masafumi
Publication year - 1999
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(19981130)125:3<40::aid-eej5>3.0.co;2-v
Subject(s) - feature (linguistics) , adaptive neuro fuzzy inference system , process (computing) , inference , computer science , layer (electronics) , artificial neural network , fuzzy inference , artificial intelligence , fuzzy inference system , backpropagation , fuzzy logic , data mining , fuzzy control system , philosophy , linguistics , chemistry , organic chemistry , operating system
A new model for generalized fuzzy inference neural networks (GFINN) is proposed in this paper. The networks consist of three layers: an input‐output layer, an if layer, and a then layer. In each layer, there are the operational nodes. A GFINN can perform three representative fuzzy inference methods by changing the connectivity and the operational nodes. There are three learning processes in a GFINN: a self‐organizing process, a rule‐integration process, and a LMS learning process. In the rule‐integration process, a GFINN employs two feature maps in order to integrate appropriate rules effectively. Computer simulations were carried out to show the superiority of a GFINN over back‐propagation networks. © 1998 Scripta Technica, Electr Eng Jpn, 125(3): 40–49, 1998