Enhanced back-propagation learning and its application to business evaluation

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.<
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