A structural learning algorithm for multi-layered neural networks
Manabu Kotani, Akihiro KAJIKI, K. Akazawa · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
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.