A Neural Network for Adaptive Learning with Mechanism of Forgetting

Abstract

Although there are some neural network models which show adaptive self-organizing characteristics, they have complicated architectures and complicated actions, and there comes to be many parameters which the values are very difficult to be adjusted.
In this paper, we propose a new neural network model for adaptive learning by unsupervised learning. The model consists of simple architacture and so has simple action compared with others. Learning of analog input is also possible by only one representation of input vectors. Moreover, by changing the parameter value, we can make the model forget old patterns. Simulation results indicate the effectiveness of the proposed model.