Programming Hierarchical TS Fuzzy Systems

In this paper, we focus on an evolutionary algorithm to design hierarchical or multilevel fuzzy system (architecture and parameters) automatically. This research work presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the if-then rule's parameters encoded in the structure is accomplished using particle swarm optimization (PSO). The proposed method interleaves both PIPE and PSO optimizations. Except for the randomly initialized hierarchical structure and parameters, we further explore the embedding of a clustering algorithm to speed up the learning algorithm. The new method results in a smaller rule-base and good learning ability. The proposed hierarchical TS-FS is evaluated by using Mackey-Glass chaotic time-series forecasting problem. When compared to other hierarchical TS-FS the proposed approach exhibits competing results with high accuracy and smaller size of hierarchical architecture