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