Abstract
This study proposes a symbiotic particle swarm optimization (SPSO) algorithm for compensatory neural fuzzy networks (CNFN). The CNFN model using compensatory fuzzy operators makes fuzzy logic systems more adaptive and effective. The proposed SPSO algorithm adopts a multiple swarm scheme that uses each particle to represent a single fuzzy rule and each particle in each swarm evolves separately to avoid falling into a locally optimal solution. Additionally, the SPSO embeds the symbiotic evolution scheme in a specific particle swarm optimization (PSO) to accelerate the search and increase global search capacity. Finally, the proposed CNFN with SPSO (CNFN-SPSO) method is applied to control a water bath temperature system. Results of this study demonstrate the effectiveness of the proposed CNFN-SPSO method.
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