Intelligent Modeling Of Complex Systems And Control Methods

Intelligent control tactics are adopted to lucubrate with temperature control system of anode baking furnace of Baiyin aluminum plant for the traits of difficult modeling and control of complex system in this paper. Fuzzy neural network (FNN) is composed of neural network and fuzzy logic system, and it is the organic integration of the two parts. FNN is the network structure, which can deal with the abstract information and has strong self-learning function and self-tuning function. It can map the relationship of input and output of the object independing on the model. In addition, it has also good fault freedom and robustness. Making use of FNN, we may get very good effect of the approximation and modeling of the random nonlinear plants and good effect of the control of the uncertain models. So, in this paper we combine the T-S model RBF neural network with the advanced control method-predictive control to predict, model and control the complex systems through theoretically analyzing the control algorithm of T-S model RBF neural network in detail and doing a mass of simulation research.A dynamic learning method of T-S fuzzy based RBF neural network is proposed on the basis of studying T-S fuzzy model and RBF neural network, by which the learning method of RBF NN is improved. The number of hidden layer nodes of T-S fuzzy RBF net is not only modified dynamically, but also the data centers and extended constants of gaussian radial basis function are changed adaptively during learning progress, moreover the algorithm can train effectively the parameters of T-S model. In addition, the algorithm also improve FNN learning performance, quicken convergence speed of the net, and generalization ability and convergence ability of the algorithm are enhanced.Due to better approximation ability of T-S fuzzy RBF neural network for nonlinear systems, T-SFRBFNN control is combined with predictive control to develop a FNN predictive control model syncretized logical inference ability of fuzzy control with strong learning ability of NN. Simulation results show that the control method has better predictive ability, adaptive ability and higher control precision.On the basis of the theory research and simulation, through processing a mass of collected input data and output data of temperature control system of anode baking furnace, we build the predictive model of baking furnace burning system and find out the relationship of control output of the firebox and temperature distributing in the fire way, which makes temperature distributing inside the furnace and calefactive velocity satisfy the technical need and offer theory foundation for producing anode of good quality.