Title :
A fast algorithm for training a class of fuzzy neural networks
Author :
Dongmei, Li ; Junqiang, Liu ; Hengzhang, Hu
Author_Institution :
Harbin Inst. of Technol., China
Abstract :
A fast algorithm for training a class of fuzzy neural networks (FNN) is studied. The proposed algorithm is called least square-simplex (LS-simplex). The algorithm obtains the performance of global convergence and avoids the inherent local convergence when adopting a grads algorithm to train the FNN, also it accelerates the FNN´s training and can be used online which is impossible when using a genetic algorithm (GA). Compared with the grads algorithm and GA, the LS-simplex owns more accurate precision and faster convergent speed, and the FNN obtained has excellent generalization performance
Keywords :
convergence; fuzzy neural nets; learning (artificial intelligence); least squares approximations; linear programming; search problems; convergent speed; fast algorithm; generalization performance; global convergence; least square-simplex; precision; Acceleration; Convergence; Fuzzy neural networks; Genetic algorithms; Least squares methods;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.863351
