Fuzzy Neural Network for Clustering and Classification
Archana R. Shinde,  Prof. D.B. Kshirsagar
This paper presents the implementation of Fuzzy Neural Network (FNN) for clustering and classification. Fuzzy neural network combines the advantage of both fuzzy logic and neural network. This paper mainly focuses on implementation of two algorithms. First algorithm is General Fuzzy min max Neural Network (GFMM) training algorithm which combines the processing of supervised and unsupervised data in a single training algorithm. Second algorithm is Data Core Based Fuzzy min max Neural Network (DCFMN) which considers the characteristics of the data and influence of noise simultaneously. These two algorithms provide high accuracy, flexibility, better performance, strong robustness in classification and clustering.
Keywords- Classification, Clustering, Fuzzy systems, Fuzzy min-max neural networks ,Data core, Overlapped neuron, Pattern classification, Hyperboxes, Hyperbox expansion, Hyperbox contraction, Overlapping neurons, Classifying neurons ,Membership function, Robustness
Cite this Article
Archana R. Shinde,  Prof. D.B. Kshirsagar,   "Fuzzy Neural Network for Clustering and Classification"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 3, pp., July 2015, Available at :http://www.ijedr.org/papers/IJEDR1503031.pdf