In this paper, an interactively recurrent functional neural fuzzy network (IRFNFN) with fuzzy differential evolution (FDE)
learning method was proposed for solving the control and the prediction problems. The traditional differential evolution
(DE) method easily gets trapped in a local optimum during the learning process, but the proposed fuzzy differential
evolution algorithm can overcome this shortcoming. Through the information sharing of nodes in the interactive layer,
the proposed IRFNFN can effectively reduce the number of required rule nodes and improve the overall performance of
the network. Finally, the IRFNFN model and associated FDE learning algorithm were applied to the control system of the
water bath temperature and the forecast of the sunspot number. The experimental results demonstrate the effectiveness
of the proposed method.
