DocumentCode
2680021
Title
Neural network and its ancillary techniques as applied to power systems
Author
El-Sharkawi, M.A.
Author_Institution
Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA
fYear
1995
fDate
34809
Firstpage
42430
Lastpage
42435
Abstract
The layered perceptron neural net is receiving the most attention as a viable candidate for application to power systems. The layered perceptron is taught by example. Before neural networks can gain the necessary recognition as useful problem solving tools in the power industry, certain fundamental issues need to be addressed. Some of them are associated with neural network technology, and others are problem dependent. The author discusses the following issues: learning versus memorisation; best net size determination, network saturation, feature extraction, neural net inversion, genetic based neural nets, and fuzzified neural nets
Keywords
learning (artificial intelligence); multilayer perceptrons; power system analysis computing; best net size determination; feature extraction; fuzzified neural nets; genetic based neural nets; layered perceptron neural net; learning; network saturation; neural net inversion; power systems; problem solving tools;
fLanguage
English
Publisher
iet
Conference_Titel
Artificial Intelligence Applications in Power Systems, IEE Colloquium on
Conference_Location
London
Type
conf
DOI
10.1049/ic:19950495
Filename
477911
Link To Document