Artificial neural networks: a tutorial (original) (raw)

Numerous e orts have been made in developing \intelligent" programs based on the Von Neumann's centralized architecture. However, these e orts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a number of scienti c disciplines are designing arti cial neural networks (ANNs) to solve a variety of problems in decision making, optimization, prediction, and control. Arti cial neural networks can be viewed as parallel and distributed processing systems which consist of a huge number of simple and massively connected processors. There has been a resurgence of interest in the eld of ANNs for several years. This article intends to serve as a tutorial for those readers with little or no knowledge about ANNs to enable them to understand the remaining articles of this special issue. We discuss the motivations behind developing ANNs, basic network models, and two main issues in designing ANNs: network architecture and learning process. We also present one of the most successful application of ANNs, namely automatic character recognition.