Application of RBF neural network in fault diagnosis in chemical industry

In this paper, the model and algorithm of neural network are performed. Special emphasis has been placed on the principle and training algorithm of radial basis function. Then the approaches to fault diagnosis for chemical industry by using a RBF neural network have been implemented. At the same time, the result of performance is given compared with BP algorithm. It demonstrates that the learning rate of RBF neural network is rapid, and online monitoring and diagnosis is feasible.