An Improved Perceptron based Illative Network for the Fault Signal Diagnosis

The faults signal diagnoses is one of the complex process for intelligent systems. Based on the principle of multi-layer neural network, this paper presents an improved algorithm with adaptive learning rate factors for the learning process of multi-layer perception (MLP). The improved algorithm is applied to the learning of an illative network for the faults signal diagnosing process. The simulations show the improved algorithm has good effects on speeding up learning process and bettering its learning convergence and robust performance.