Enhanced back-propagation learning and its application to business evaluation

Author

Arisawa, Masaki ; Watada, Junzo

Author_Institution

Dept. of Ind. Manage., Osaka Inst. of Technol., Japan

Volume

1

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

155

Abstract

The error backpropagation learning algorithm of layered neural networks have several weak points including: 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 less than the conventional method. Employing the method in a 4-bits parity check problem, its effectiveness is shown. Finally, as an application of the enhanced learning algorithm of the neural network to a real problem, the neural model of business evaluation based on financial indices was built and its learning time was shorten up to 64% less than the conventional one

Keywords

backpropagation; business data processing; feedforward neural nets; performance evaluation; 4-bits parity check; business evaluation; error backpropagation; layered neural networks; learning algorithm; learning time; Biological neural networks; Education; Electronic mail; Gradient methods; Learning systems; Neural networks; Neurons; Parity check codes; Technology management; Termination of employment;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374155

Filename

374155