Different Pattern Classifier Techniques Based on Fuzzy Min-Max Neural Network

Abstract: For classification of patterns, various neural networks related to Fuzzy Min-Max (FMM) have been studied. An Enhanced Fuzzy Min-Max (EFMM) neural network is most recent. EFMM Neural Network classifier that utilizes fuzzy sets as pattern classes has been studied. The contribution of EFMM is ability to overcome a number of limitations of the original FMM network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. A survey on Pattern Classification based on Fuzzy Min-Max Neural Network has been done and presented in this paper.

Keywords: ANN, MLP, FMM, EFMM, GFMM, ARC, MFMM.