Research On Neural Fuzzy System And Its Application In Coordinated Control System Of Power Plant

Neural Fuzzy System (NFS) is a new fuzzy inference system structure that combined fuzzy logic system with neural networks. It is the neural network realization for Takagi and Sugeno fuzzy inference system. It can approach any linear and nonlinear function with any precision, but also quicken converging speed, decrease precise errors, and lesser training samples that are needed. This paper researched the learning ability, generalization ability, method of input selection, overfitting problem and dimension disaster of NFS. In the end, NFS was applied to the modeling and control of Coordinated Control System (CCS) that is complex system of power plant to validate the validity of NFS. 1,The effect that the combination of input, the number of membership function of input, training times and training sample affect the learning ability and generalization ability of NFS was researched respectively. The relation between the learning ability and generalization ability was studied. 2,A performance index of error was presented. This index is a kind of evaluation of neural fuzzy model performance and synthetically considered training error and checking error of NFS. 3,A new method of input selection for NFS was proposed. This method is easy, fast, and can directly obtain n, m and td of the object system. Another advantage is that precedence order of input selection indicates the importance grade of input influencing output. 4,As for the overfitting problem of NFS, this paper proposed a method of selecting training sample and checking sample and a method of selecting the most befitting training times. 5,As for the complex system modeling based on Neural Fuzzy System, This paper (1) researched the structure and learning algorithm for the Multistage Neural Fuzzy System (MNFS); (2) presented a method of input selection based on MNFS; (3) presented a method of building the model of the multi-output systems based on MNFS. 6,Because of the large time-delay of CCS of Boiler-turbine unit, this paper designed two-stage neural fuzzy system to build the models of CCS. NFS was respectively used to build the nonlinear model, the linear model and the NFS model built according to the field data of the CCS. The simulation results show that the models based on NFS have higher identification precision and less predictive error. 7,For the CCS of Boiler-turbine unit, using NFS as a controller, this paper designed two control methods: (1) the neural fuzzy CCS based on Linear Quadratic Regulator(LQR) control system; (2) the neural fuzzy CCS based on PID-decoupling control system. The simulation results show that the control systems based on NFS have better control performance and stronger robustness.