DocumentCode :
2428668
Title :
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
Link To Document :
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