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Research on the Robust Identification of Fuzzy Model and Its Application

Author: WangJia
Tutor: WangHongWei
School: Dalian University of Technology
Course: Systems Engineering
Keywords: Fuzzy Modeling Robust identification Objective function H_ ∞ error estimates Linear matrix inequalities
CLC: N945.14
Type: Master's thesis
Year: 2009
Downloads: 72
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Abstract


Due to the actual complex industrial process often has a strong nonlinearity , uncertainty , multi- variable , strong coupling , as well as conditions change frequently , it is difficult to establish a precise mathematical model description , even if they can establish its mathematical model , often too complex , making traditional control is difficult to achieve the desired control effect . The fuzzy set theory proposed by Zadeh accurate mathematical model of the complexity of the expression can not be used or morbid system provides an effective modeling method . Test data to the input and output of the system based on to determine fuzzy rules , the establishment of a system of fuzzy model . Tightly around the fuzzy modeling and identification of nonlinear systems to discuss and research . First, the nonlinear system fuzzy modeling process , due to the presence of uncertainties such as noise and coupling makes the description obtained through the use of fuzzy clustering fuzzy relationship matrix , its columns may be serious linear , and so there must some redundant rules . How to find these rules , simplification rules , compression of the input space of fuzzy model fuzzy modeling problem . Selected according to the objective function fuzzy model of the structure of a nonlinear system fuzzy modeling methods , the convergence of the method and theorem proving . Secondly, fuzzy modeling applied to nonlinear system the H_ ∞ identification method of the time-domain field , makes interference to the estimation error of the maximum energy gain to a minimum, and solved using linear matrix inequalities unknown energy in the identification process factor γ, to overcome the shortcomings of the actual project value of γ is usually given by the empirical value of uncertainty or complexity given by the iterative algorithm . Finally, the identification method using fuzzy fuzzy modeling of power station emulator turbine generator seal oil cooling system , simulation and experimental results show that the fuzzy modeling method proposed in this paper can effectively establish a nonlinear system model , the model than good accuracy. Relative to other modeling methods in terms of the model interpretability .

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CLC: > SCIENCE AND > Journal of Systems Science > Systems Engineering > Systems Analysis > System identification
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