Automatic Design of Hierarchical TS-FS Model Using Ant Programming and PSO Algorithm

  • Yuehui Chen
  • Jiwen Dong
  • Bo Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)


This paper presents an approach for designing of hierarchical Takagi-Sugeno fuzzy system (TS-FS) automatically. The hierarchical structure is evolved using Ant Programming (AP) with specific instructions. The fine tuning of the rule’s parameters encoded in the structure is accomplished using Particle Swarm Optimization (PSO) algorithm. The proposed method interleaves both optimizations. Starting with random structures and rules’ parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it fine tunes its rules’ parameters. It then goes back to improving the structure again and, provided it finds a better structure, it again fine tunes the rules’ parameters. This loop continues until a satisfactory solution (hierarchical Takagi-Sugeno fuzzy model) is found or a time limit is reached. The performance and effectiveness of the proposed method are evaluated using time series prediction problem and compared with the related methods.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Jiwen Dong
    • 1
  • Bo Yang
    • 1
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R.China

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