Title :
An algorithm to determine neural network hidden layer size and weight coefficients
Author :
Peng, Kemao ; Ge, Shuzhi S. ; Wen, Chuanyuan
Author_Institution :
Dept. of Autom. Control, Beijing Univ. of Aeronaut. & Astronaut., China
Abstract :
The strictly decreasing relationship between the sample approximation error and the number of hidden units in a three layer artificial feedforward neural network (AFNN) is proven in the sample space. The relationship is a powerful tool in determining the number of hidden units needed. A hybrid optimization algorithm is proposed on the relationship for simultaneously determining the number of hidden units and weight coefficients in the AFNN. The algorithm is the synthesis of golden section, evolutionary programming and gradient based algorithm which is effective in determining the number of hidden units and weight coefficients in the neural network
Keywords :
feedforward neural nets; genetic algorithms; gradient methods; learning (artificial intelligence); evolutionary programming; feedforward neural network; gradient method; hidden units; learning; optimization; weight coefficients; Approximation error; Artificial neural networks; Ash; Convergence; Error analysis; Estimation theory; Genetic programming; Heuristic algorithms; Network synthesis; Neural networks;
Conference_Titel :
Intelligent Control, 2000. Proceedings of the 2000 IEEE International Symposium on
Conference_Location :
Rio Patras
Print_ISBN :
0-7803-6491-0
DOI :
10.1109/ISIC.2000.882934
