Efficient strategies for error updating to improve performance backpropagation learning

There exists a neuron oscillation generated among neurons of the output layer and pattern oscillation generated due to correlation between patterns in error backpropagation learning. Because such oscillations have different features and originate in a mutually correlative situation, there exists the phenomenon that learning time lengthens considerably and convergency is fallen in the existing method that solves two oscillations by means of one learning strategy. In this paper, the authors propose learning strategies that correspond to the feature of each oscillation and apply a learning strategy that is suitable for the problem adaptively when learning a given problem. In order to show the effectiveness of the proposed learning strategies, the authors compared them with existing methods by applying them to 4-6 parity problems, seven segment display and pattern recognition. With the result that, learning time decreased considerably and convergence increased remarkably from the existing methods.