Multi-resolution fuzzy ART neural networks
Penny Pei Chen, Wei‐Chung Lin, Hai-Lung Hung · 2003
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.