Approximation and Prediction Using Neural Network

Abstract

Numerous advances have been made in developing intelligent programs, some inspired by biological neural networks. Researchers from many scientific disciplines are designing artificial neural networks (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control. Although successful conventional applications can be found in certain well-constrained environments, none is flexible enough to perform well outside its domain. ANNs provide exciting alternatives, and many applications could benefit from using them. This article is for those readers with little or no knowledge of ANNs to help them understand the other articles in this issue of this journal. It discusses the motivation behind the development of ANNs, describes the basic biological neuron and the artificial computation model, outlines network architectures and learning processes. This paper includes an interesting example approximation and prediction for a teacher evaluation system using neural network. The author also explains how to effectively use Matlab software to successfully apply the modeling and simulation techniques presented.