Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 75-81.doi: 10.11896/jsjkx.240500086

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TSK Fuzzy System Enhanced by TSVR with Cooperative Parameter Optimization

WANG Wei, ZHAO Yunlong, PENG Xiaoyu, PAN Xiaodong   

  1. School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China
  • Received:2024-05-21 Revised:2024-09-05 Published:2025-07-17
  • About author:WANG Wei,born in 1999,postgra-duate.Her main research in-terest is fuzzy mathematics and its application.
    PAN Xiaodong,born in 1979,Ph.D,associate professor,Ph.D supervisor.His main research interests include fuzzy information processing,fuzzy set theory and so on.
  • Supported by:
    National Natural Science Fundation of China(12301595,62106206).

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Abstract: As a special nonlinear regression system,the Takagi-Sugeno-Kang (TSK) fuzzy system can solve machine learning tasks,but its effect on high-dimensional problems is not ideal,and it is difficult to determine and adjust the rules.In order to optimize the system,the fuzzy “IF-THEN” rule is followed.Firstly,the fuzzy clustering algorithm is used to divide the dataset,and the data points are mapped to the space representing the membership degree from the point to the fuzzy clustering center.Secondly,the twin support vector regression machine (TSVR) is used to determine the two regression planes to obtain the regression values.Considering that different datasets adapt to different key parameters such as cluster number,the genetic algorithm (GA) is used to optimize multiple parameters at the same time,which simplifies the prior setting of domain knowledge.All of the above processes are called as TSVR-GA-TSK fuzzy system (TG-TSK).Experimental results show that compared with the classical regression algorithms and the typical TSK fuzzy systems,the TG-TSK fuzzy system has good regression accuracy and robustness,and has a significant advantage in the pairwise comparison of Nemenyi test.

Key words: Takagi-Sugeno-Kang(TSK) fuzzy system, Twin support vector regression machine, Genetic algorithm, Cooperative optimization, Regression task

CLC Number: 

  • TP273.4
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