Adaptive pattern recognition by self-organizing neural networks

Sunanda Mitra, Yong Soo Kim, Surya Pemmaraju

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Nonlinear decision boundaries separating similar regions in unlabeled data sets are encountered in many real applications but are extremely difficult to generate by statistical decision models. Supervised multilayered neural networks are capable of generating some nonlinear decision boundaries but require extensive training procedures leading to computational burden as well. Unsupervised self-organizing neural nets are capable of forming crisp clusters from unlabeled data sets. A recent trend has been to integrate the concept of fuzzy sets with adaptive learning inherent to neural nets. Such integration yields not only better clustering of similar data groups, it may also provide a method for generating nonlinear boundaries among clusters in close proximity. The weaknesses and strengths of such integrated self-organizing neuro-fuzzy models for adaptive pattern recognition are described. Successful classification of standard data sets and generation of nonlinear decision boundaries among neighboring clusters from computer generated data are demonstrated with a recently developed integrated adaptive fuzzy clustering (IAFC) model.

Original languageEnglish
Article number1031209
Pages (from-to)185-214
Number of pages30
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Jun 28 1994
EventNeural and Fuzzy Systems: The Emerging Science of Intelligent Computing 1994 - Bellingham, United States
Duration: Jan 1 1994 → …


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