Study On Fuzzy Model Identification And Output Prediction Based On Kernel Method

Fuzzy model identification is one of the most important research branches in intelligent control theory. Traditional mathematical modeling approaches are always not amenable to various complex research objects which come up with the developing information technology. However, fuzzy model possesses some advantages such as expressing structural knowledge easily and being able to combine mathematical function approximators with process information, etc. Many new approaches based on fuzzy model have been successfully applied in fields such as system identification, intelligent control and pattern recognition, etc. Thus, fuzzy model identification has become one of the key issues in basic theory research of intelligent control.This dissertation focuses on developing a novel and effective identification algorithm which can overcome some shortages existing in conventional methods by introducing kernel methods to the field of fuzzy model identification.In this paper, the main works are:(1) we develop a new support vector machine(SVM)-based fuzzy identification algorithm, which uses SVM to achieve structure identification to improve generalization capability, and utilizes Kalman filtering to implement the parameter estimation.(2)In this paper, there is a new program that contains GPC method and Kernelized Least Squares method to estimate forecast output of model better. Estimate output with control volume by Kernelized Least Squares method, the simulations prove this method could reduce error between actual output and forecast output and improves accuracy of forecast output significantly.(3)To overcome the rule redundancy which probably exists in the support vector fuzzy system, we develop a novel identification algorithm based on dual kernel-based learning machines(kernel fuzzy clustering and support vector regression),and propose a combination strategy for support vectors to guarantee the conciseness. This approach simultaneously avoids the shortcoming that the performance of traditional clustering-based identification algorithm is sensitive to the initial clustering number,...