Pattern recognition using a hierarchical neural network

Pau Choo Chung, E. Liang Chen, Ching Tsorng Tsai

研究成果: Paper

1 引文 斯高帕斯(Scopus)

摘要

A pattern recognition system based on hierarchical neural networks is proposed in this paper. The hierarchical system consists of two levels of networks: low-level for feature extraction and high-level for object recognition. The low-level is a Competitive Hopfield Neural Network (CHNN) which detects the dominant points of a target shape to be the pattern features, based on the minimization of a cost function. The CHNN is implemented by incorporating a winner-take-all strategy in the network. By imposing the winner-take-all rule, we are relieved from the suffering of deciding the suitable values of the weighting factors in the cost function. Furthermore, from the experimental results, we also find that the proposed CHNN performs very well in determining the dominant points of a target shape. After the features have been extracted, they are applied to the high-level multilayered network for object recognition. Because the multilayer network has high robustness to the pattern variations, the recognition system is found to posses high noise tolerance capability. Experimental results show that the system can recognize all the objects correctly when the percentage of noises is under 10%. Even when the percentage of noises reaches 40%, the recognition ratio is still over 90%.

原文English
頁面3104-3109
頁數6
出版狀態Published - 1994 十二月 1
事件Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
持續時間: 1994 六月 271994 六月 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
城市Orlando, FL, USA
期間94-06-2794-06-29

    指紋

All Science Journal Classification (ASJC) codes

  • Software

引用此

Chung, P. C., Chen, E. L., & Tsai, C. T. (1994). Pattern recognition using a hierarchical neural network. 3104-3109. 論文發表於 Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, .