Title :
A RBF network modeling approach combining GA and orthogonal optimum seeking method
Author :
Yanjun, Li ; Wu Tie-jun ; Mingwang, Zhao
Author_Institution :
Nat. Lab. for Ind. Control Technol., Zhejiang Univ., Hangzhou, China
Abstract :
This paper proposes a novel approach for the training of RBF neural networks. It combines the genetic algorithm (GA) and orthogonal optimum seeking method, taking advantage of both the GA´s global optimization ability and the orthogonal optimum seeking method that can quickly determine the numbers of the hidden nodes, to correctly determine the centers of the RBF network. Simulation results show that it is a simple method, and is more reliable and efficient than orthogonal optimum seeking method in training the RBF neural network for dynamic modeling
Keywords :
genetic algorithms; learning (artificial intelligence); radial basis function networks; dynamic modeling; genetic algorithm; global optimization; hidden nodes; learning; orthogonal optimum seeking method; radial basis function neural network; Genetic algorithms; Industrial control; Industrial training; Laboratories; Neural networks; Optimization methods; Radial basis function networks;
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.2000.863358
