Optimization of Interval Type-2 Fuzzy Logic System By using A New Hybrid Method of Whale Optimization algorithm and Extreme Learning Machine
The problem of searching for the best values of the fuzzy logic parameters (T1FLS) is consider complex problems, and for type-2 fuzzy logic system (T2FLS) the problem is more complex, in special case interval type-2 fuzzy logic system (IT2FLS). The Researchers have used many methods and algorithms to solve this problem, and among the most important algorithms used in this field are the)Meta-heuristic) algorithms. Because Meta-heuristic algorithms have a high capacity in the practical field, so we used one of the modern algorithms in this field, which is the Whale Optimization algorithm (WOA). We are used the (WOA) algorithm together with the Extreme Learning Machine (ELM) algorithm as a hybrid algorithm to find the best parameters for the IT2FLS. Whereas, the (WOA) algorithm was used to estimate the values of the antecedent for the system, and the (ELM) algorithm was used to find the values of the consequent parts in the system. The simulation results show that the proposed algorithm is effective for a system (IT2FLS).
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