Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlin...Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlinearity, uncertainty, multiple variables and couple action. It is difficult or even impossible to effectively deal with this kind of system with the existing conventional system and control theories based on classical logic. The theory of fuzzy sets and fuzzy systems open a new alternative way to modeling, analysis and control of such systems. But most developments are limited during the dealing with SISO systems in recent years. Therefore, the study on multivariable fuzzy system is of significance in respects of theory and application, and becomes one of the focuses on the research of the fuzzy logic techniques. In this dissertation, several conclusions about the multivariable fuzzy system theory have been achieved. The whole thesis includes two parts, and the main contents and conclusions are summarized as follows: In the first part, the theory about modeling, analysis and control of multivariable fuzzy systems is studied, including 1 The study on generalized fuzzy basis function based multivariable fuzzy system model By analyzing the existing modeling methods of multivariable fuzzy systems, enlightened by the fuzzy cell to cell mapping model proposed by L.M.Jia, a new analytical description of the MIMO fuzzy rules generalized fuzzy basis function (GFBF) is put forwards under the deterministic definition of the fuzzy cellization. It cannot only simultaneously the numerical data and linguistic knowledge of the complex systems, but also contains many kinds of fuzzy basis function according to the basic properties of GFBF. Consequently, generalized fuzzy basis function series (GFBFS), an efficient and concise modeling method for MIMO fuzzy systems, is proposed through the reasonable selection for the decision making logic used in the fuzzy inference mechanism, which can be proved to approximate arbitra展开更多
文摘Modeling, analysis and control of multivariable fuzzy system, an important direction of intelligent techniques, are to analyze and deal with complex MIMO dynamic systems. A complex multivariable system features nonlinearity, uncertainty, multiple variables and couple action. It is difficult or even impossible to effectively deal with this kind of system with the existing conventional system and control theories based on classical logic. The theory of fuzzy sets and fuzzy systems open a new alternative way to modeling, analysis and control of such systems. But most developments are limited during the dealing with SISO systems in recent years. Therefore, the study on multivariable fuzzy system is of significance in respects of theory and application, and becomes one of the focuses on the research of the fuzzy logic techniques. In this dissertation, several conclusions about the multivariable fuzzy system theory have been achieved. The whole thesis includes two parts, and the main contents and conclusions are summarized as follows: In the first part, the theory about modeling, analysis and control of multivariable fuzzy systems is studied, including 1 The study on generalized fuzzy basis function based multivariable fuzzy system model By analyzing the existing modeling methods of multivariable fuzzy systems, enlightened by the fuzzy cell to cell mapping model proposed by L.M.Jia, a new analytical description of the MIMO fuzzy rules generalized fuzzy basis function (GFBF) is put forwards under the deterministic definition of the fuzzy cellization. It cannot only simultaneously the numerical data and linguistic knowledge of the complex systems, but also contains many kinds of fuzzy basis function according to the basic properties of GFBF. Consequently, generalized fuzzy basis function series (GFBFS), an efficient and concise modeling method for MIMO fuzzy systems, is proposed through the reasonable selection for the decision making logic used in the fuzzy inference mechanism, which can be proved to approximate arbitra
