研究目的
To explore an identification problem for nonlinear models and propose a novel fuzzy identification method based on the ant colony optimization algorithm to establish the Takagi-Sugeno fuzzy model with better approximation results and robustness.
研究成果
The proposed fuzzy identification method combines a modified cluster validity criterion index and ACO and OLS algorithms to establish the T-S fuzzy model with minimal error variance between the established model and the original nonlinear model. Examples demonstrate that the proposed method provides better approximation results and robustness than existing methods.
研究不足
The study does not explicitly mention technical and application constraints or potential areas for optimization.
1：Experimental Design and Method Selection:
The study adopts a modified cluster validity criterion with a fuzzy c-regression model to find appropriate rule numbers of the Takagi-Sugeno fuzzy model and uses the ant colony optimization algorithm to obtain the sifted initial membership function and consequent parameters.
2：Sample Selection and Data Sources:
The study uses input-output data from nonlinear models for identification.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The methodology involves determining the number of fuzzy rules, identifying membership function parameters, establishing the consequent structure, optimizing initial consequent parameters, and identifying consequent parameters.
5：Data Analysis Methods:
The study uses an improved fuzzy c-regression model and the orthogonal least-squares method for data analysis.
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