Competition competence evaluation of power generating enterprises using improved neural network

被引：0
|
作者：
Sun, Wei [1 ]
Shang, Wei [1 ]
Qi, Jian-xun [2 ]
机构：
[1] North China Elect Power Univ, Sch Business & Adm, Baoding 071003, Peoples R China
[2] North China Elect Power Univ, Sch Business & Adm, Beijing 102206, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
enterprise competition competence; evaluation index; improved BP neural network; dynamic control;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the development of electricity market reformation in China, it is especially important to evaluate the competition competence of power generating enterprises. Based on the characteristics of their, this paper bring forwards an index system to evaluate the competition competence of power generating enterprises. Improved BP neural network is introduced which is aimed at feedback lag and easily falling into the local optimal minimum. From dynamic controlling quotients, the methods at input layer and out layer errs is improved. The computation time is much shorter than tradition algorithms and the precision is relatively improved. The results show that the improved algorithm effectively reduces the training fluctuation and improves the speed. The optimum result could more easily get.
引用
收藏
页码：2959 / +
页数：2
相关论文
共 50 条
  • [21] An approach to enterprises credit evaluation based on BP neural network
    Sun, QW
    Wu, JX
    Luan, XH
    [J]. FOURTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS: THE INTERNET ERA & THE GLOBAL ENTERPRISE, VOLS 1 AND 2, 2005, : 803 - 808
  • [22] The Learning Evaluation of Learning Enterprises Based on BP Neural Network
    Zhang, Yong
    [J]. SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING: THEORY AND PRACTICE, VOL 2, 2012, 115 : 819 - 826
  • [23] The application of Balanced Scorecard and Neural Network on the Performance Evaluation for Enterprises
    Gu, Yao
    Guo, Lan
    Sun, Qingyu
    [J]. 2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 105 - +
  • [24] Generating Power-optimal Standard Cell Library Specification Using Neural Network Technique
    Lim, S. H.
    Lim, Y. W.
    Mashohor, S.
    Kamsani, N. A.
    Sidek, R. M.
    Hashim, S. J.
    Rokhani, F. Z.
    [J]. 2017 IEEE ASIA PACIFIC CONFERENCE ON POSTGRADUATE RESEARCH IN MICROELECTRONICS AND ELECTRONICS (PRIMEASIA), 2017, : 101 - 104
  • [25] Evaluation method of a new power system construction based on improved LSTM neural network
    Si, Weiguo
    Lin, Weifang
    Xu, Daolin
    Luo, Yuanbo
    Han, Ninghui
    [J]. JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2022, 22 (05) : 1819 - 1832
  • [26] Research on the Enterprises Credit Evaluation based on BP Neural Network
    Luan Xiaohui
    Shang Liwei
    [J]. PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INNOVATION & MANAGEMENT, VOLS I AND II, 2007, : 1150 - 1154
  • [27] Generating new patterns for information gain and improved neural network learning
    Viktor, HL
    [J]. IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL IV, 2000, : 529 - 534
  • [28] RETRACTED: Supply Capability Evaluation of Intelligent Manufacturing Enterprises Based on Improved BP Neural Network (Retracted Article)
    Quan, Quan
    Zhang, Zhongqiang
    [J]. JOURNAL OF MATHEMATICS, 2022, 2022
  • [29] Health evaluation of power transformer using deep learning neural network
    Xing, Zhikai
    He, Yigang
    Chen, Jianfei
    Wang, Xiao
    Du, Bolun
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2023, 215
  • [30] Neural network and improved method for wind power prediefion
    Li, Rui
    [J]. PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND SOFTWARE ENGINEERING (ICAISE 2013), 2013, 37 : 199 - 203
12345