Neural networks: an engineering perspective

Author

Klimasauskas, Casimir C Casey

Volume

30

Issue

9

fYear

1992

Firstpage

50

Abstract

The development and application of neural networks are presented from an engineering perspective. It is stated that neural computing is a collection of mathematical techniques that have been gaining growing acceptance as plug-compatible replacements for statistical and other data-modeling techniques. Two of these techniques, function approximation and clustering, are discussed. The forces shaping the future of neural networking systems, including plug compatibility, hybrid systems-neural computing concepts integrated with expert systems, fuzzy logic, and genetic algorithms-and application specification systems, are reviewed.<>

Keywords

expert systems; function approximation; fuzzy logic; genetic algorithms; neural nets; pattern recognition; clustering; expert systems; function approximation; fuzzy logic; genetic algorithms; hybrid systems; mathematical techniques; neural computing; neural networks; plug compatibility; Adaptive signal processing; Clustering algorithms; Computer hacking; Function approximation; Kernel; Multilayer perceptrons; Nervous system; Neural networks; Power engineering and energy; Signal processing algorithms;

fLanguage

English

Journal_Title

Communications Magazine, IEEE

Publisher

ieee

ISSN

0163-6804

Type

jour

DOI

10.1109/35.156804

Filename

156804