Enhanced learning in neural networks and its application to financial statement analysis
M. Arisawa, Junzo Watada · 1994
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. >