DocumentCode :
315248
Title :
A structural learning algorithm for multi-layered neural networks
Author :
Kotani, Manabu ; Kajiki, Akihiro ; Akazawa, Kenzo
Author_Institution :
Fac. of Eng., Kobe Univ., Japan
Volume :
2
fYear :
1997
fDate :
9-12 Jun 1997
Firstpage :
1105
Abstract :
We propose a new structural learning algorithm for organizing the structure of the multi-layered neural networks. The proposed pruning algorithm consists of two already known algorithms, the structural learning algorithm with forgetting and the optimal brain damage algorithm using the second derivatives of the assessment. After the network is slimmed by the structural learning algorithm with forgetting, unimportant weights are pruned from the network using the second derivatives. The simulations are performed for the Boolean function and the acoustic diagnosis of compressors. The results show that the proposed algorithm is effective for eliminating the unimportant weights
Keywords :
acoustic signal processing; compressors; fault diagnosis; learning (artificial intelligence); multilayer perceptrons; Boolean function; acoustic diagnosis; forgetting; multi-layered neural networks; optimal brain damage algorithm; pruning algorithm; structural learning algorithm; unimportant weights; Acoustic propagation; Biological neural networks; Boolean functions; Brain modeling; Convergence; Iterative algorithms; Multi-layer neural network; Neural networks; Pattern recognition; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
Type :
conf
DOI :
10.1109/ICNN.1997.616184
Filename :
616184
Link To Document :
بازگشت