In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO), combined symbiotic evolution and modified particle swarm optimization for tuning parameters of the RFNFN. Simulation results show that the converging speed and root mean square error (RMS) of the proposed method has a better performance than those of other methods. (22 refs)
