Title :
Neural networks: an engineering perspective
Author :
Klimasauskas, Casimir C Casey
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;
Journal_Title :
Communications Magazine, IEEE
