Operator Based On Compensation Fuzzy Neural Network System And Its Applied Research, System Modeling And Control

Abstract--In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is adopted to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by fuzzy neruons can not only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of nonlinear system modeling have shown that 1 )The compensatory neurofuzzy system can effectively learn commonly used fuzzy IFI-LEN rules from either well-defined initial data or ill-defined data; 2)The convergence speed of the compensatory learning algorithm is faster than that of the convertional backpropagation algorithm; 3) The efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree. In addition, the method to apply the above network in control field is introduced in details. In the end, the compensatory neurofuzzy system is used to identify a typical nonlinear unit,control a complex nonlinear object CSTR. From simulation, an excellent result is gotten.