NDLI: Multi-resolution fuzzy ART neural networks

Content Provider	IEEE Xplore Digital Library
Author	Penny Pei Chen Wei-Chung Lin Hai-Lung Hung
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Northwestern Univ. IL, USA (Penny Pei Chen)
Abstract	This paper proposes a new neural network model, a multi-resolution fuzzy ART (MRF-ART), which employs fast competitive learning and efficient parallel matching to solve complex data classification problems. The architecture of MRF-ART not only preserves the ART-type neural network characteristics but also extends their capability to represent input patterns in a hierarchical fashion. To achieve this, an MRF-ART network uses multiple output layers arranged in a cascaded manner which is completely different from a conventional fuzzy ART network with only one output layer. Moreover, the parallel matching process enables the parallel hardware implementation of an MRF-ART. To demonstrate the data representational capability of an MRF-ART network, we applied it to two data sets and the results indicated that fine-to-coarse data representation can be achieved.
Starting Page	1973
Ending Page	1978
File Size	665178
Page Count	6
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.832686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Subspace constraints Neural networks Supervised learning Unsupervised learning Artificial neural networks Pattern recognition Multi-layer neural network Hardware Training data
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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