Effectiveness of artificial neural networks adaptation according to time period of training data acquisition

Abstract

Artificial neural networks (ANNs) were inspired by natural neural networks (NNNs) and natural processes of training. The NNNs receive data in time still tuning the inner model of the surrounding world. These valuable features of our brains let us to dynamically accommodate themselves to the changes surround. These features make us possible to forget some irrelevant information, correct our knowledge and meet truth. ANNs usually work on the training data (TD) acquired in the past and totally known at the beginning of the adaptation process. Because of this the adaptation methods of the ANNs can be sometimes more effective than the natural training process observed in the NNNs. This paper discusses the ability of ANNs to adapt more effectively than NNNs do if only the TD is completely given at the beginning of the adaptation process. In this case the adaptation process of ANNs can be divided into two steps: analyze or examining the set of TD and construction of neural network topology and weights computation. Two different applications areas of such approach are presented in the paper.