Journal of Petrochemical Universities

Journal of Petrochemical Universities ›› 2007, Vol. 20 ›› Issue (3): 86-88.

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Radial Basis Function Neural Netw ork Algo ri thm and I ts Applicat ion

  

  1. (Department of Control Science & Engineering,Huazhong University of Science & Technology, Wuhan Hubei 430074,P.R.China)
  • Received:2007-02-05 Online:2007-09-20 Published:2017-07-05

RBF 神经网络算法及其应用

张顶学, 刘新芝, 关治洪   

  1. (华中科技大学控制科学与工程系, 湖北武汉430074)
  • 作者简介:张顶学(1975 -), 男, 湖北潜江市, 在读博士
  • 基金资助:
    国家自然科学基金资助项目(60573005)。

Abstract:

Based on the study of radial basis function (RBF) neural network training algorithm, a new RBF neural network training algorithm was introduced by combining genetic algorithm of chromosomes with changeable length and least-square method. It is able to determine the structure and parameters of network. The new training algorithm was used to model heat loading forecasting for co-generation power plants, compares with BP neural network and RBF neural network based on a training algorithm of automatic increase in hidden nodes. Simulation results show that the proposed method is valid.

Key words:

摘要: 在径向基神经网络学习算法的基础上, 提出了一种新的RBF 神经网络学习算法, 该算法将变长度染
色体遗传算法和最小二乘法相结合, 能够同时确定径向基神经网络的结构和参数。用此方法建立热电厂热负荷预
测模型, 并与BP 神经网络和增长型结构学习算法的RBF 神经网络方法相比较, 结果表明可以取得更好的效果。

关键词: RBF 神经网络 , 遗传算法 , 最小二乘法 , BP 神经网络 , 热负荷

Cite this article

ZHANG Ding-xue,LIU Xin-zhi,GUAN Zhi-hong. Radial Basis Function Neural Netw ork Algo ri thm and I ts Applicat ion[J]. Journal of Petrochemical Universities, 2007, 20(3): 86-88.

张顶学, 刘新芝, 关治洪. RBF 神经网络算法及其应用[J]. 石油化工高等学校学报, 2007, 20(3): 86-88.

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