Integrated Genetic Neural Networks and Its Application in Fault Diagnosis

Abstract

In order to over come the problems about slow rate of convergence and falling easily into part minimums in BP algorithm, a new improved genetic BP algorithm was put forward. To determine whether the network fall into part minimum point, a discriminant of part minimum was put forth in the training process of neural network. Genetic algorithm was used to revise the weights of the neural network if the BP algorithm fell into minimums. The integrated genetic neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which taking the sub-genetic neural network as primary diagnosis from different sides, then gained the conclusions through decision-making fusion. It can be educed from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.