Enhanced learning in neural networks and its application to financial statement analysis

Masaki Arisawa, Junzo Watada

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

It is discussed that layered neural networks have several weak points in the learning algorithm of error back-propagation such as terminating at a local optimal solution and requiring its learning for many hours. In this paper an enhanced method for learning algorithm is proposed in order to shorten the learning time more than a conventional method. Employing the method in a 4 bits parity check problem, its effectiveness is shown. At the end, as the application of the enhanced learning algorithm of the neural network to the real problem, the neural model for the financial statement analysis based on financial indices is discussed and its effectiveness is shown.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Neural Networks - Conference Proceedings
Place of PublicationPiscataway, NJ, United States
出版社IEEE
ページ3686-3692
ページ数7
6
出版ステータスPublished - 1994
外部発表はい
イベントProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
継続期間: 1994 6 271994 6 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94/6/2794/6/29

ASJC Scopus subject areas

  • Software

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