Fuzzy Neural Network Technique And Its Applications To Process Control

Along with the development of technology there are complexity and uncertainty of control systems in industry manufacture process. Many uncertain factors, such as correlation, randornicity, will change incidentally when environment and time change. So traditional control technique based on mathematical model is unuseful. New control strategy has being seeking. Fuzzy control is a kind of human imitating technique which is independent on the controlled plant mathematical model. It utilizes the knowledge and experience of experts to carry out rationalization. As a result, it has good robustness. But it is lack of the ability of self-learning or self-tuning after the fuzzy control rules have been set off-line. Neural network has the ability of self-learning in spite of its nonlinear mapping similar with Black-Box. The abilities of self-learning and expression of the whole system will be improved when they hand together. The research of this combination is in the ascendant. First, the backgrounds, improvements and principles of fuzzy control, neural networks and fuzzy neural networks are introduced. Facing the characteristics of multi-variable, nonlinear plant, this thesis established a fuzzy neural network which construct fuzzy subject function and fuzzy rational control model. As a result, the parameters of neural network have equivalent fuzzy meanings. New arithmetic combining self-organization with supervision is applied in FNNC which can detect initial- subject function and optimize rules. On the basis of fuzzy neural network technique, this thesis does research work of simulation on multi-variable side-wind for chemical fiber air conditioning temperature and humidity control system, and gives out the simulation curve of FNN control. The results of simulation show that this strategy is feasible in industry, so it has good perspective of industrial application. The other important work in this thesis is mainly about the design of intelligent air conditioning system. The background, significance, hardware selection, design, and software design are introduced in detail. The results of system experiment show that the whole system satisfy the design request.