Abstract
A novel tuning and rule reduction algorithm is introduced as a design tool for the development of knowledge-based fuzzy neural controllers. A Hybrid Approach to Fuzzy Supervised Learning Algorithm (HAFSLA) that combines the Multiobjective Genetic Algorithms (MGA), Gradient Descent (GD) method and Least Square Estimation (LSE) technique is used to obtain the optimised fuzzy models. Simulations demonstrate that the proposed robust control has successfully met the design specifications for efficiency and robustness, respectively.
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