Neural fuzzy inference systems with knowledge-based cultural differential evolution for nonlinear system control

Abstract

This study proposes a knowledge-based cultural differential evolution (KCDE) method for neural fuzzy inference systems (NFIS). Cultural algorithms acquire the belief space from the evolving population space and then exploit that information to guide the search. The proposed KCDE method adopts the mutation strategies of differential evolution as knowledge sources to influence the population space. The proposed KCDE method uses these knowledge sources, including normative knowledge, situational knowledge, domain knowledge, history knowledge, and topographic knowledge, to optimize the parameters of the NFIS model to avoid falling in a local optimal solution and to ensure the searching capacity of approximate global optimal solution. Experimental results demonstrate that the proposed NFIS-KCDE method performs well in nonlinear system control problems.