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2006

Minimal Structure of Self-Organizing HCMAC Neural Network Classifier

9 years 9 months ago
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
The authors previously proposed a self-organizing Hierarchical Cerebellar Model Articulation Controller (HCMAC) neural network containing a hierarchical GCMAC neural network and a self-organizing input space module to solve high-dimensional pattern classification problems. This novel neural network exhibits fast learning, a low memory requirement, automatic memory parameter determination and highly accurate high-dimensional pattern classification. However, the original architecture needs to be hierarchically expanded using a full binary tree topology to solve pattern classification problems according to the dimension of the input vectors. This approach creates many redundant GCMAC nodes when the dimension of the input vectors in the pattern classification problem does not exactly match that in the self-organizing HCMAC neural network. These redundant GCMAC nodes waste memory units and degrade the learning performance of a self-organizing HCMAC neural network. Therefore, this study pres...
Chih-Ming Chen, Yung-Feng Lu, Chin-Ming Hong
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where NPL
Authors Chih-Ming Chen, Yung-Feng Lu, Chin-Ming Hong
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