Optimizing Learning Algorithm For Rbf Neural Network Applied To Pickling Process Systems

In this article, Aiming at pickling process heated by power microwave heating device system is well controlled which it is an uncertain, complex and nonlinear plant. Firstly, RBF (Radial Basis Function) neural network is introduced simply. Secondly, several currently norm learning algorithm of RBF neural network to approximation function and its training speed are compared. Recently, by the method applying the identification control of the microwave drying device and the predicting model of energy consumption, the performance is very nice. Thirdly, conclusions are arrived at and they provide some theoretical references for controlling the microwave device systems and optimizing their technical parameters in practices.