Abstract
This paper proposes recurrent fuzzy system design using elite-guided continuous ant colony optimization (ECACO). The designed recurrent fuzzy system is the Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN), in which each fuzzy rule contains feedback loops to handle dynamic system processing problems. The ECACO optimizes all of the free parameters in each recurrent fuzzy rule in a TRFN. Unlike the general ant colony optimization that finds solutions in discrete space, the ECACO finds solutions in a continuous space. The ECACO is a population-based optimization algorithm. New solutions are generated by selection, Gaussian random sampling, and elite-guided movement. To verify the performance of ECACO, three examples of dynamic plant control are simulated using ECACO-optimized TRFNs. The ECACO performance is also compared with other continuous ant colony optimization, particle swarm optimization, and genetic algorithms in these simulations.
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