东北电力技术2016，Vol.37Issue(7)：39-43,5.
基于改进ELM神经网络的客户满意度评价模型
Evaluation Modal on Customer Satisfaction of Electric P ower Based on Improved ELM Neural Network
卢海明 1刘建鑫2
作者信息
- 1. 广州地铁集团有限公司运营事业总部，广东 广州 510310
- 2. 国网江西省电力公司萍乡供电分公司，江西 萍乡 337000
- 折叠
摘要
Abstract
A dynamic recurrent neural network, namely ELM neural network simulating the assessment of expert scoring has been used to evaluate the electric power customer satisfaction. The calculation of real examples shows that this method is capable to reflect the lev⁃els of customer satisfaction accurately with the advantages of fast training speed and simple structure. At the same a method for optimi⁃zing the connecting weight value coefficient of ELM neural network is presented by using the global searching ability of IWO. The opti⁃mization of neural network parameters is a large scale multimodal optimization problem and the tests show that IWO has obvious advan⁃tages in solving high⁃dimensional multimodal optimization problem particularly. This new approach combines the merits of ELM neural network that has the ability of dynamic memory and the strong global searching capability of IWO which exactly makes up the shortcom⁃ings of single algorithm. The simulations reveal that neural network optimized by IWO is able to build a higher precision modal for the e⁃valuation of electric power customer satisfaction and guarantee the uniformity and stability of expert evaluating system.关键词
电力客户满意度/入侵杂草算法/神经网络/电力市场Key words
Electric power customer satisfaction/Invasive weed optimization/Neural network/Electricity market分类
管理科学引用本文复制引用
卢海明,刘建鑫..基于改进ELM神经网络的客户满意度评价模型[J].东北电力技术,2016,37(7):39-43,5.
