Role of Different Fuzzy Min- Max Neural Network for Pattern Classification
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

Review Papers | Computer Science & Engineering | India | Volume 3 Issue 12, December 2014

Role of Different Fuzzy Min- Max Neural Network for Pattern Classification

Jaitra Chakraborti

Different neural networks related to Fuzzy min-max (FMM) has been studied and amongst all, Enhanced Fuzzy min-max (EFMM) neural network is most recent. For classification of patterns a new Enhanced Fuzzy Min-Max (EFMM) algorithm has been studied. The aim of EFMM is to improve the performance and minimize the restrictions that are possessed by original fuzzy min-max (FMM) network. Three heuristic rules are used to improve the learning algorithm of FMM. First, to eliminate the problem of overlapping during hyperbox expansion, new overlapping rules has been suggested. Second, to discover other overlapping cases the hyperbox test rule has been extended. Third, to resolve the hyperbox overlapping cases, hyperbox contraction rule is provided.

Keywords: Fuzzy minmax FMM model, hyperbox structure, neural network learning, online learning, pattern classification

Edition: Volume 3 Issue 12, December 2014

Pages: 1343 - 1346

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How to Cite this Article?

Jaitra Chakraborti, "Role of Different Fuzzy Min- Max Neural Network for Pattern Classification", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB14624, Volume 3 Issue 12, December 2014, 1343 - 1346

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