Multi-resolution fuzzy ART neural networks

Penny Pei Chen*, Wei Chung Lin, Hai Lung Hung

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


This paper proposes a new neural network model, Multi-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 fuzz 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.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages6
StatePublished - Dec 1 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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