A recent trend in developing adaptive decision models has been to integrate the concept of fuzzy membership functions of data samples with adaptive learning inherent to neural nets. Several different approaches have been suggested for such integration involving Adaptive Resonance Theory as well as Kohonen self-organizing neural networks. Such neuro-fuzzy models appear to be quite effective in successful clustering of complex data samples encountered in many pattern recognition and control applications where traditional decision models fail due to lack of knowledge of data distributions and unavailability of training data sets. The strengths and weaknesses of currently existing ART-based neuro-fuzzy models are described.
|Number of pages||5|
|State||Published - 1994|
|Event||Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA|
Duration: Jun 27 1994 → Jun 29 1994
|Conference||Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)|
|City||Orlando, FL, USA|
|Period||06/27/94 → 06/29/94|