Title :
A simplified fuzzy model based on grey relation and data transformation techniques
Author :
Huang, Yo-Ping ; Chu, Hung-Chi
Author_Institution :
Dept. of Comput. Sci. & Eng., Tatung Inst. of Technol., Taipei, Taiwan
Abstract :
Unlike previous methods which relied on the given data, the paper depends on preprocessed patterns to construct a satisfactory fuzzy model. The grey relational method is exploited to help determine the most appropriate premise variables for the fuzzy rules such that a simplified modeling procedure can be achieved. There is no need to refine the membership functions used in the fuzzy rules. Only the transformation functions and the consequent real numbers are required to be adjusted to satisfy the identification purpose. To validate how well the consequent singletons are adjusted, the refined parameters are compared with the desired values calculated by the least squares method. The results show that the adjusted parameters from the proposed model conform to the desired ones. Simulation results from different examples are presented to demonstrate the superiority of the proposed model over conventional methodologies
Keywords :
fuzzy set theory; knowledge based systems; least squares approximations; uncertainty handling; data transformation techniques; fuzzy rules; grey relation; grey relational method; least squares method; membership functions; premise variables; preprocessed patterns; refined parameters; simplified fuzzy model; simplified modeling procedure; transformation functions; Computer science; Convergence; Data engineering; Differential equations; Fuzzy systems; Genetic algorithms; Input variables; Optimization methods; Performance analysis; Takagi-Sugeno model;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.633295