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 :
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