The Simulation Of ANFIS By MATLAB And The Study Of Hydraulic Pressure Machine Fuzzy Temperature Control System

The system of control plants already has quite clear math model and mature traditional control method, but there are some accident changes during the operation of control plants, such as the change of the plants parameter, the change of the load parameter .The emergence of these factors make the PID controller that cannot attend to one thing without losing sight of the other and reduce the robustness of the system .The Fuzzy control can adequately use his special property of nonlinearity and vary configuration to overcome the nonlinearity and vary parameter factors of the control system, and raise the system's robustness, The Fuzzy Control belongs to the control of information, and the regular fuzzy control has the innate defect .That is , some measures must be taken to improve the control precision and overcome the subjectivity of fuzzy modeling.In this paper, firstly,a fuzzy inference system based on neural network learning algorithm is presented, and it is applied to identify the tempetature system. Then the abilities of fuzzy inductive inference and self-learning are realized by constructing Adaptive Network-based Fuzzy Inference System(ANFIS) that combines fuzzy control and neural network together. ANFIS uses neural network to realize fuzzy inference. This make it has ability of fuzzy inductive inference and ability of tuning the way of inference. Since the construction of ANFIS has clear meaning, the design and initialization of ANFIS are also very easy. Thirdly, after analyzing the advantage and disadvantage of various kinds of learning algorithm of the fuzzy system, a hybrid learning algorithm is introduced. Finally, the subcluser algorithm is used to initialize the ANFIS's parameters. Then the back-propagation algorithm is used to adjust the parameters on-line, and the result of the simulation experiments shows that ANFIS has good control performances.However, the algorithm of ANFIS is too complex to be carried out in the condition of the limit number of MCGS' variables, so we must take some measures to simply the fuzzy system and improve the control precision at same time.Secondly, the cause of fuzzy control with low precision is analyzed in detail, and a new-type fuzzy control method is proposed. It does not depend on the accurate mathematics model, can overcome the effects of the nonlinear, coupling and uncertain factors, especially with its on-line inference . In accordance with 3500T hydraulic...