Improvement of classification accuracy by using enhanced query-based learning neural networks

Shyh Jier Huang, Ching Lien Huang

研究成果: Paper

1 引文 斯高帕斯(Scopus)

摘要

An enhanced query-based learning neural network is proposed for the dynamic security control of power systems. Compared to conventional neural network, the enhanced query-based learning provides a classifier at lower computational cost. This methodology requires asking a partially trained classifier to respond to the questions. The response of the query is then taken to the oracle. An oracle is responsible for providing better quality of training data. The regions of classification ambiguity will thus be narrowed. It can be seen that the proposed method is intrinsically different from learning by randomly generated data. With only a small amount of additional complexity, the enhanced query-based neural network approach greatly increases the classification accuracy of neural networks.

原文English
頁面398-402
頁數5
出版狀態Published - 1996 一月 1
事件Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
持續時間: 1996 六月 31996 六月 6

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
城市Washington, DC, USA
期間96-06-0396-06-06

    指紋

All Science Journal Classification (ASJC) codes

  • Software

引用此

Huang, S. J., & Huang, C. L. (1996). Improvement of classification accuracy by using enhanced query-based learning neural networks. 398-402. 論文發表於 Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4), Washington, DC, USA, .