Compensatory neural fuzzy networks with rule-based cooperative differential evolution for nonlinear system control

This study presents a rule-based cooperative differential evolution (RCDE) learning algorithm to optimize the parameters of a compensatory neural fuzzy network (CNFN) for solving control problems. CNFN uses the compensatory degree for fuzzy reasoning to dynamically adjust fuzzy operators that can make the fuzzy system more adaptive and effective. RCDE decomposes the fuzzy system into multiple rule-based subpopulations where each subpopulation represents a fuzzy rule set, and each individual within each subpopulation evolves by differential evolution (DE) separately. The proposed RCDE uses cooperative behavior among multiple subpopulations for combining their information and building the complete fuzzy system to accelerate the search and increase global search capacity. Finally, the experimental results show that the proposed RCDE method better approximates the global optimal solution and has a faster convergence rate than the other methods.