Application And Simulation Of Neural Network In System Modeling

Neural network is very effective to identify and approximate nonlinear dynamical system. It is applied widely and successfully in engineering area. Many researchers model process based neural network to predict or optimize yield.This paper presents two kinds of forward neural network: BP (Back-Propagation) and RBF (Radial Basis Function), and the learning algorithms are also introduced . Then the concept of steel annealing is expounded. And neural-based solution of modeling and optimization of steel annealing process is given. Nonlinear function approximation using BP neural network and RBF neural network is simulated. We propose some modifications of the back-propagation algorithm to speed up the convergence rate and increase the possibility to escape local minima. In this paper, we discuss detailedly about how to determine the number of hidden layer of neural network, about how to select learning samples, about generalization of neural network. At last, the algorithm to find the optimal value is given.