| Along with the development of technology there are complexity and uncertainty of control systems in industry manufacture process. Many uncertain factors, such as correlation, randomicity, 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's 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. For the high controlled condition for larger nonlinear system, the identification which the classed method is difficult is important. The paper purposed a new associative memory neural network which can replaced system and ready for the request of control. This paper on the basis of fuzzy neural network technique and optimization it's parameter can get ideal output. The results of simulation show that this strategy is feasible in industrial, so it has good perspective of industrial application.In industrial the temperature system is classed large nonlinear system. With associative memory neural network as identification and fuzzy neural network as control established model reference adaptive control system, succeeded realized single dealing control. Model reference adaptive control system supplied new method for other lager nonlinear system such as flux, stress and fluid system.
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