[1]薛晓岑,向文国,吕剑虹.基于差分进化与RBF神经网络的热工过程辨识[J].东南大学学报(自然科学版),2014,44(4):769-774.[doi:10.3969/j.issn.1001-0505.2014.04.016]
 Xue Xiaocen,Xiang Wenguo,Lü Jianhong.Thermal process identification based on differential evolution and RBF neural network[J].Journal of Southeast University (Natural Science Edition),2014,44(4):769-774.[doi:10.3969/j.issn.1001-0505.2014.04.016]
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基于差分进化与RBF神经网络的热工过程辨识()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
44
期数:
2014年第4期
页码:
769-774
栏目:
自动化
出版日期:
2014-07-16

文章信息/Info

Title:
Thermal process identification based on differential evolution and RBF neural network
作者:
薛晓岑向文国吕剑虹
东南大学能源与环境学院, 南京 210096
Author(s):
Xue Xiaocen Xiang Wenguo Lü Jianhong
School of Energy and Environment, Southeast University, Nanjing 210096, China
关键词:
热工过程 系统辨识 径向基函数 差分进化 建模
Keywords:
thermal processes system identification radial basis function differential evolution modeling
分类号:
TP183
DOI:
10.3969/j.issn.1001-0505.2014.04.016
摘要:
针对热工过程的非线性辨识问题,提出了一种基于差分进化算法(DE)的径向基函数神经网络(RBFNN)模型设计方法.该方法将DE算法的种群分解为几组并行的子种群,每组子种群对应于一类隐节点数相同的RBF网络.在RBFNN的学习过程中进行多子种群并行优化,从而实现RBF网络结构与参数的同时调整.算法可以利用热工对象的输入输出数据,自动设计出满足误差精度要求且结构较小的RBFNN模型.然后将该算法应用于热工对象的辨识,对于单输入单输出系统,得到的RBFNN模型只需1个隐节点.对于多输入单输出系统,RBF网络也仅需较少的隐层节点.仿真结果表明,用该方法设计的RBFNN模型结构简单,且辨识误差小,具有较好的泛化能力.
Abstract:
For the nonlinear identification of thermal process, a new radial basis function neural network(RBFNN)design method is proposed based on the differential evolution algorithm(DE). In the method, the population in the DE algorithm is divided into several parallel subpopulations, and each subpopulation corresponds to a class of RBF network solutions with the same hidden nodes. In the RBFNN learning process, the network structure and parameters are adjusted simultaneously through the parallel optimization of the subpopulations. Under the given error limit, the algorithm can design an RBF model automatically with fewer hidden nodes according to thermal input and output data. Then, the algorithm is used to identify nonlinear thermal processes. For single-input single-output system identification, only one node is required in the RBFNN hidden layer. For multi-input single-output system identification, the RBFNN model also requires less hidden nodes. The simulation results show that the proposed approach can achieve the given identification accuracy with fewer hidden nodes, and has good generalization ability.

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备注/Memo

备注/Memo:
收稿日期: 2013-12-11.
作者简介: 薛晓岑(1986—),男,博士生;向文国(联系人),男,博士,教授,博士生导师, wgxiang@seu.edu.cn.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2006AA05A113-1).
引用本文: 薛晓岑,向文国,吕剑虹.基于差分进化与RBF神经网络的热工过程辨识[J].东南大学学报:自然科学版,2014,44(4):769-774. [doi:10.3969/j.issn.1001-0505.2014.04.016]
更新日期/Last Update: 2014-07-20