Self-Learning of Probability Distribution Function by Multilayered Perceptions.

Overlearning in the neural network can cause a failure in the approximation of a function because of the limited generalization power of the neural network. To solve this problem, it is considered effective that the neural network learns the probability density function of the learning samples, and the result "answer is impossible" is obtained when an input is given in the region containing no learning sample. This paper proposes a method in which the probability density of the learning sample is reflected on the output for the function by alternately inputting the learning sample and the random value. It is reported in this paper through a simulation that the proposed method works correctly in the multilayered perception (MLP). When this method is applied, there is a close relation among the SN ratio, the output norm and the probability density of the learning samples. [ABSTRACT FROM AUTHOR]