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.

A structural learning algorithm for multi-layered neural networks

Intelligent diagnostic system for the rachet mechanism faults detection using acoustic analysis

Describes the acoustic diagnosis technique for a compressor using a hybrid neural network (HNN). The HNN is composed of two neural networks, an acoustic feature extraction network using a backpropagation network (BPN) and a fault discrimination network using a Gaussian potential function network (GPFN). The BPN is composed of five layers and the number of the middle hidden units is smaller than the others. The target patterns for the output layer are the same as the input patterns. After the learning of the network, the middle hidden layer acquires the compressed input information. The input patterns of the GPFN are the output values of the middle hidden layer in the BPN. The task of the HNN is to discriminate four conditions of the valve under various experimental conditions. As a result, 93.6% discrimination accuracy is obtained in this experiment. This suggests that the proposed model is effective for the acoustic diagnosis.

T. Kanagawa

Papers

Hybrid neural networks for acoustic diagnosis