Title :
Evolutionary Algorithm Based Radial Basis Function Neural Network for Function Approximation
Author :
Kuo, R.J. ; Hu, Tung-Lai ; Chen, Zhen-Yao
Author_Institution :
Dept. of Ind. Manage., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan
Abstract :
This study attempts to enhance the performance of radial basis function neural network (RBFnn) using self- organizing map neural network (SOMnn). In addition, the hybrid of genetic algorithm and particle swarm optimization (HGP) algorithm is employed to train RBFnn for function approximation. The proposed SOM-HGP evolutionary algorithm combines the automatically clustering ability of SOMnn and the HGP algorithm. Experimental results for three continuous test functions show that the algorithm has the best performance than GA [21], PSO [8], HPSGO [15] for training RBFnn.
Keywords :
evolutionary computation; function approximation; genetic algorithms; learning (artificial intelligence); mathematics computing; particle swarm optimisation; pattern clustering; radial basis function networks; self-organising feature maps; RBFnn function approximation; RBFnn training; SOM-HGP evolutionary algorithm; automatical clustering ability; continuous test function; evolutionary algorithm; genetic algorithm based optimization algorithm; particle swarm optimization; radial basis function neural network; self- organizing map neural network; Approximation algorithms; Clustering algorithms; Evolutionary computation; Function approximation; Genetic algorithms; Neural networks; Organizing; Particle swarm optimization; Radial basis function networks; Testing;
Conference_Titel :
Bioinformatics and Biomedical Engineering , 2009. ICBBE 2009. 3rd International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-2901-1
Electronic_ISBN :
978-1-4244-2902-8
DOI :
10.1109/ICBBE.2009.5162810
