The neural network of linear approximation

In this paper we consider a three-layer neural network, carrying out a polygonal approximation of the training set of data. In the process of supervised learning, neural network divides the input training set of data into n-dimensional simplex. We construct a hyperplane approximating output training signals for each simplex. This paper presents algorithm of learning a neural network. Neural network can be used to solve problems of prediction, recognition and classification of images.