Design of Fuzzy Respective Space-Based Neuro-Fuzzy Networks for Pattern Recognition

In this paper, we introduce the design of fuzzy respective space-based neuro-fuzzy networks for pattern recognition. The proposed networks are realized by partitioning of the fuzzy respective input space to generate the fuzzy rules. The respectively partitioned spaces using fuzzy respective input space express the rules of the networks. The consequence part of the rules is represented by polynomial functions. The coefficients of consequence part of the rules are learned by the back-propagation algorithm. And we also optimize the proposed networks using real-coded genetic algorithms. A numerical example is given to evaluate the validity of the proposed networks for pattern recognition. As a result, this paper shows that the proposed networks have the good result together with fewer rules.