[1]熊伟丽,陈敏芳,王 肖,等.运用改进差分进化算法辨识Hammerstein模型[J].南京理工大学学报(自然科学版),2013,37(04):536.
 Xiong Weili,Chen Minfang,Wang Xiao,et al.System identification method for Hammerstein model based on improved differential evolution algorithm[J].Journal of Nanjing University of Science and Technology,2013,37(04):536.
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运用改进差分进化算法辨识Hammerstein模型
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年04期
页码:
536
栏目:
出版日期:
2013-08-31

文章信息/Info

Title:
System identification method for Hammerstein model based on improved differential evolution algorithm
作者:
熊伟丽12陈敏芳2王 肖2徐保国2
江南大学 1.教育部轻工过程先进控制重点实验室; 2.物联网工程学院,江苏 无锡 214122
Author(s):
Xiong Weili12Chen Minfang2Wang Xiao2Xu Baoguo2
1.Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education); 2.School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China
关键词:
差分进化算法 自适应变异 非线性系统辨识 Hammerstein模型
Keywords:
differential evolution algorithm adequate variation nonlinear system identification Hammerstein model
分类号:
TP18
文献标志码:
A
摘要:
针对非线性系统Hammerstein模型,利用差分进化算法对非线性模型进行参数辨识,将非线性系统的辨识问题转化为参数空间上的函数优化问题。为了增强差分进化算法的辨识性能,采用一种自适应变异差分进化算法,即引入一个自适应变异率,随着迭代的进行自适应调整缩放因子,从而在初期保持种群多样性避免早熟; 在后期逐步降低变异率,保留优良信息,避免最优解遭到破坏。最后通过仿真对比实验表明,改进的差分进化算法比基本差分进化算法精度更高、非线性辨识能力更强。
Abstract:
For the nonlinear system of Hammerstein model,a method for nonlinear system identification is proposed based on the differential evolution algorithm(DE).The problem of nonlinear system identification is transformed into an optimization problem in parameter space.In order to enhance the performance of the DE identification,this paper proposes an adaptive mutation differential evolution algorithm(MDE).The parameter of the Hammerstein model in early stage can keep the individuals diversifying to avoid premature convergence.The mutation rate is gradually reduced so as not to damage the optimal solution.The MDE algorithm is more accurate than the DE,and the MDE algorithm has the higher nonlinear recognition ability.

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备注/Memo

备注/Memo:
收稿日期:2012-07-30 修回日期:2012-10-18
基金项目:国家自然科学基金(21206053,21276111); 中国博士后基金(2012M511198); 江苏高校优势学科建设工程资助项目(PAPD)
作者简介:熊伟丽(1978-),女,博士,副教授,主要研究方向:复杂工业过程建模及优化; 智能优化算法及应用,E-mail:greenpre@163.com。
更新日期/Last Update: 2013-08-31