Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm

This paper proposes zero-order Takagi–Sugeno–Kang (TSK)-type fuzzy system learning using a two-phase swarm intelligence algorithm (TPSIA). The first phase of TPSIA learns fuzzy system structure and parameters by on-line clustering-aided ant colony optimization (ACO). Phase two aims to further optimize all of the free parameters in the fuzzy system using particle swarm optimization (PSO). In clustering-aided ACO (CACO), fuzzy system structure is learned through on-line clustering. Once a new rule is generated by clustering, the consequent is selected from a discrete set of candidate values by ACO. In ACO, the path of an ant is regarded as a combination of consequent values selected from every rule. CACO helps to locate good initial fuzzy systems for subsequent phase learning. In Phase two, initial particles in PSO are randomly generated according to the best solution found by CACO. All free parameters in the designed fuzzy system are optimally tuned by PSO. Simulations on fuzzy control of three nonlinear plants are conducted to verify TPSIA performance. Comparisons with other learning algorithms demonstrate TPSIA performance.