The neural network of linear approximation

Author

Kozynchenko, Vladimir A. ; Prus, Anna I.

Author_Institution

St. Petersburg State Univ., St. Petersburg, Russia

fYear

2014

fDate

June 30 2014-July 4 2014

Firstpage

77

Lastpage

78

Abstract

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.

Keywords

approximation theory; learning (artificial intelligence); neural nets; hyperplane; image classification; image prediction problem; image recognition; input training data set; linear approximation; n-dimensional simplex; neural network learning; output training signal approximation; polygonal approximation; supervised learning; three-layer neural network; Biological neural networks; Educational institutions; Linear approximation; Neurons; Piecewise linear approximation; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Technologies in Physical and Engineering Applications (ICCTPEA), 2014 International Conference on

Conference_Location

St. Petersburg

Print_ISBN

978-1-4799-5315-8

Type

conf

DOI

10.1109/ICCTPEA.2014.6893289

Filename

6893289