NDLI: Application of Genetic LVQ Neural Network in Credit Analysis of Power Customer

Content Provider	IEEE Xplore Digital Library
Author	Jing-min Wang Yu-qian Wen
Copyright Year	2008
Description	Author affiliation: North China Electr. Power Univ., Baoding (Jing-min Wang; Yu-qian Wen)
Abstract	Difficulties in collection of electric charge have affected the regular operation and development of power supply bureau seriously. So the credit problem of power customer has become one of the focus questions that power supply bureau pays attention to. In this paper, learning vector quantization (LVQ) neural network is used to establish the credit analysis model of power customer. And an improved genetic algorithm (NGA) is adopted to set initial reference vectors in competition layer of LVQ. Then, it solves two problems of LVQ neural network. That is, the neurons are not utilized adequately and LVQ network is sensitive to the initial data. A comparison study is reported based on LVQ with random initial reference vectors and LVQ with initial reference vectors set by NGA. Simulation results have shown that the proposed method enhances the accuracy and speed of credit classification. So, it is promising to credit analysis of power customer.
Starting Page	305
Ending Page	309
File Size	392839
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power supplies Neurons LVQ Artificial neural networks Power customer Genetic algorithms Intelligent networks Power measurement Neural networks Genetic algorithm Computer applications Computer networks Credit analysis Artificial intelligence
Content Type	Text
Resource Type	Article

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