Title :
Composition methods of fuzzy neural networks
Author :
Horikawa, Shin-ichi ; Furuhashi, Takeshi ; Okuma, Shigeru ; Uchikawa, Yoshiki
Author_Institution :
Dept. of Electron. Mech. Eng., Nagoya Univ., Japan
Abstract :
Fuzzy neural networks (FNNs) are systems which apply neural networks to fuzzy reasoning. Two types of FNN are presented. In the first type, the consequences of fuzzy reasoning are realized by constants. In the second type, the consequences are expressed by first-order linear equations. The FNNs can automatically identify fuzzy rules and tune membership functions. Their performance on fuzzy reasoning is examined by simulations. The features of the two types of FNNs are clarified
Keywords :
fuzzy logic; neural nets; first-order linear equations; fuzzy neural networks; fuzzy reasoning; fuzzy rule identification; membership function tuning; neural network composition; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Input variables; Marine vehicles; Mechanical engineering; Neural networks; Nonlinear systems;
Conference_Titel :
Industrial Electronics Society, 1990. IECON '90., 16th Annual Conference of IEEE
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-87942-600-4
DOI :
10.1109/IECON.1990.149317
