The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition

摘要

In this paper, a neuro-fuzzy network with novel hybrid learning algorithm is proposed. The novel hybrid learning algorithm is based on the fuzzy entropy clustering (FEC), the modified particle swarm optimization (MPSO), and the recursive singular value decomposition (RSVD). The FEC is used to partition the input data for performing structure learning. Then, we adopt the MPSO to adjust the antecedent parameters of fuzzy rules. Two strategies in the MPSO, called the effective local approximation method (ELAM) and the multi-elites strategy (MES), are proposed to improve the performance of the traditional PSO. Moreover, we will apply RSVD to obtain the optimal consequent parameters of fuzzy rules. The proposed hybrid learning algorithm achieves superior performance in learning speed and learning accuracy than those of some traditional genetic methods.