DMCA
Minimal Structure of Self-Organizing HCMAC Neural Network Classifier
Cached
Download Links
- [www.ie.ntnu.edu.tw]
- [nccur.lib.nccu.edu.tw]
- DBLP
Other Repositories/Bibliography
BibTeX
@MISC{Chen_minimalstructure,
author = {Chih-ming Chen and Yung-feng Lu and Chin-ming Hong},
title = {Minimal Structure of Self-Organizing HCMAC Neural Network Classifier},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
The authors previously proposed a self-organizing HCMAC (Hierarchical Cerebellar Model Articulation Controller) 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 presents a minimal structure of self-organizing HCMAC (MHCMAC) neural network with the same dimension of input vectors as the pattern classification problem. Additionally, this study compares the learning performance of this novel learning structure with those of the BP neural network, support vector machine (SVM), and original self-organizing HCMAC neural network in terms of ten
Keyphrases
minimal structure pattern classification problem input vector self-organizing hcmac neural network classifier self-organizing hcmac neural network neural network learning performance self-organizing hcmac redundant gcmac node automatic memory parameter determination full binary tree topology original self-organizing hcmac neural network accurate high-dimensional pattern classification original architecture novel neural network exhibit novel learning structure low memory requirement hierarchical gcmac neural network hierarchical cerebellar model articulation controller memory unit high-dimensional pattern classification problem support vector machine self-organizing input space module bp neural network many redundant gcmac node
