DocumentCode :
288498
Title :
Fuzzy inferencing with ART networks
Author :
Mitra, Sunanda
Author_Institution :
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX, USA
Volume :
2
fYear :
1994
fDate :
27 Jun-2 Jul 1994
Firstpage :
1230
Abstract :
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 (ART) 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
Keywords :
ART neural nets; adaptive systems; fuzzy neural nets; fuzzy set theory; inference mechanisms; learning (artificial intelligence); pattern recognition; ART networks; Adaptive Resonance Theory; adaptive learning; clustering; data samples; fuzzy inferencing; fuzzy membership functions; neural nets; neuro-fuzzy models; pattern recognition; Clustering algorithms; Computer vision; Euclidean distance; Fuzzy neural networks; Image sequence analysis; Laboratories; Neural networks; Pattern recognition; Resonance; Subspace constraints;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
Type :
conf
DOI :
10.1109/ICNN.1994.374361
Filename :
374361
Link To Document :
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