Evolution computation based learning algorithms of polygonal fuzzy neural networks

• Published in: International journal of intelligent systems Vol. 26; no. 4; pp. 340 - 352
• Main Authors: He, Chunmei, Ye, Youpei
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2011
• Subjects: Algorithms; Artificial neural networks; Fuzzy; Fuzzy logic; Fuzzy set theory; Genetic algorithms; Learning; Networks
• ISSN: 0884-8173; 1098-111X; 1098-111X
• DOI: 10.1002/int.20469

We present two fuzzy conjugate gradient learning algorithms based on evolutionary algorithms for polygonal fuzzy neural networks (PFNN). First, we design a new algorithm, fuzzy conjugate algorithm based on genetic algorithm (GA). In the algorithm, we obtain an optimal learning constant η by GA and the experiment indicates the new algorithm always converges. Because the algorithm based on GA is a little slow in every iteration step, we propose to get the learning constant η by quantum genetic algorithm (QGA) in place of GA to decrease time spent in every iteration step. The PFNN tuned by the proposed learning algorithm is applied to approximation realization of fuzzy inference rules, and some experiments demonstrate the whole process. © 2011 Wiley Periodicals, Inc.