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.
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