Title :
A genetic algorithm approach used to generate the neural network structures
Author :
Liu, Zhijun ; Sugisaka, Masanori
Author_Institution :
Dept. of Electr. & Electron. Eng., Oita Univ., Japan
Abstract :
A genetic algorithm (GA) is implemented to search for the optimal structures of neural networks which are used for approximating a given nonlinear function. Two kinds of neural networks, i.e. the multilayer feedforward and time delay neural networks are involved in the paper. The weights of each neural network in each generation are obtained by associated training algorithms. The simulation results of nonlinear function approximation are given and some improvements in the future are outlined
Keywords :
feedforward neural nets; function approximation; genetic algorithms; learning (artificial intelligence); multilayer perceptrons; neural net architecture; nonlinear functions; search problems; multilayer feedforward network; nonlinear function approximation; optimal structures; time delay neural networks; training algorithms; Artificial neural networks; Biological cells; Delay effects; Encoding; Function approximation; Genetic algorithms; Genetic engineering; Multi-layer neural network; Neural networks; Neurons;
Conference_Titel :
Intelligent Robots and Systems, 1999. IROS '99. Proceedings. 1999 IEEE/RSJ International Conference on
Conference_Location :
Kyongju
Print_ISBN :
0-7803-5184-3
DOI :
10.1109/IROS.1999.812772
