Boiler combustion efficiency optimization based on improved radial basis neural network

doi:10.3724/SP.J.1087.2013.01771

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (06): 1771-1779.DOI: 10.3724/SP.J.1087.2013.01771

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Boiler combustion efficiency optimization based on improved radial basis neural network

JIN Yuping,DANG Jie

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

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

西安科技大学计算机科学与技术学院，西安 710054

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。

Abstract

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

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

CLC Number:

TP183

JIN Yuping DANG Jie. Boiler combustion efficiency optimization based on improved radial basis neural network[J]. Journal of Computer Applications, 2013, 33(06): 1771-1779.

靳玉萍党婕. 基于径向基神经网络改进算法优化锅炉燃烧效率[J]. 计算机应用, 2013, 33(06): 1771-1779.

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Boiler combustion efficiency optimization based on improved radial basis neural network

基于径向基神经网络改进算法优化锅炉燃烧效率

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