Nonlinear system control using a fuzzy cerebellar model articulation controller involving reinforcement-strategy-based bacterial foraging optimization

Abstract

This article proposes a fuzzy cerebellar model articulation controller with reinforcement-strategy-based modified bacterial foraging optimization for solving the cart-pole balancing control problem. The proposed reinforcement-strategy-based modified bacterial foraging optimization is used to adjust the parameters of fuzzy receptive field functions and fuzzy weights for improving the accuracy of the fuzzy cerebellar model articulation controller output. An efficient strategic approach is applied in the chemotaxis step in the traditional bacterial foraging optimization algorithm. In the approach, each virtual bacterium swims for different run lengths and increases the bacterial diversity. Experimental results are presented to show the performance and effectiveness of the proposed reinforcement-strategy-based modified bacterial foraging optimization method.

Dynamic System Identification and Prediction Using a Self-Evolving Takagi–Sugeno–Kang-Type Fuzzy CMAC Network

“…A corresponding relationship is present between the input and output of the mapping. The CMAC has a highly standardized computational structure, fast network learning, local generalization, and fast convergence [5][6][7][8][9]. However, because of its constant response and quantified receptive fields, the approximation capacity of the CMAC model is limited.…”