Self-organization hybrid evolution learning algorithm for recurrent wavelet-based neuro-fuzzy identifier design

In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models.