A Self-Constructing Neural Fuzzy Network with Dynamic-Form Symbiotic Evolution

In this paper, we propose a self-constructing neural fuzzy network with dynamicform symbiotic evolution (SCNFN-DSE) for solving various problems. A novel hybrid learning approach, which consists of the self-clustering algorithm (SCA) and the dynamic-form symbiotic evolution (DSE), is proposed for adjusting the parameters of neural fuzzy networks. First, the proposed SCA is used to identify a parsimonious internal structure. The SCA is an online clustering method and is a distance-based connectionist clustering method. Second, the proposed DSE uses the sequential-search based dynamic evolution (SSDE) method. The better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic-mutation. Simulation results have shown that 1) the SCNFN-DSE model converges quickly; 2) the SCNFNDSE model requires a small number of population sizes; 3) the SCNFN-DSE model construct only 4 fuzzy models in every generation. + Corresponding author.