计算机应用 ›› 2013, Vol. 33 ›› Issue (06): 1771-1779.DOI: 10.3724/SP.J.1087.2013.01771

• 典型应用 • 上一篇    下一篇

基于径向基神经网络改进算法优化锅炉燃烧效率

靳玉萍,党婕   

  1. 西安科技大学 计算机科学与技术学院,西安 710054
  • 收稿日期:2012-12-04 修回日期:2013-02-18 出版日期:2013-06-01 发布日期:2013-06-05
  • 通讯作者: 党婕
  • 作者简介:靳玉萍(1975-),女,河南开封人,讲师,博士,主要研究方向:人工神经网络;党婕(1989-),女,陕西渭南人,硕士研究生,主要研究方向:智能优化算法。

Boiler combustion efficiency optimization based on improved radial basis neural network

JIN Yuping,DANG Jie   

  1. College of Computer Science and Technology, Xi’an University of Science and Technology,Xi’an Shaanxi 710054,China
  • Received:2012-12-04 Revised:2013-02-18 Online:2013-06-05 Published:2013-06-01
  • Contact: DANG Jie

摘要: 为了提高径向基神经网络训练精度,提出一种混合优化算法。该算法利用粒子群优化算法全局搜索能力强的特点,避免了K均值算法受初始点选择的不利影响,提高了网络中心的搜索速度;同时采用动态权值算法避免径向基神经网络可能出现的病态问题,进一步提高网络的逼近能力。锅炉燃烧实例表明了改进算法的有效性和实用性。

关键词: 锅炉燃烧, 粒子群优化算法, K均值算法, 变梯度算法

Abstract: In order to improve the training accuracy of radial basis neural network, this paper proposed a hybrid optimization algorithm. The algorithm used the strong global search ability of Particle Swarm Optimization (PSO) algorithm to avoid the adverse effect by choosing initial point in the K-means algorithm, thus improving the network center search speed. Meanwhile, the dynamic weight algorithm was used to avoid the ill-posed problem, and to further improve the network approximation ability. The boiler combustion instance indicates that the improved algorithm is efficient and practical.

Key words: boiler combustion, Particle Swarm Optimization (PSO) algorithm, K-means algorithm, conjugate gradient algorithm

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