Please wait a minute...
J4  2010, Vol. 44 Issue (1): 99-103    DOI: 10.3785/j.issn.1008-973X.2010.01.018
电子、通信与自动控制技术     
Hammerstein系统递推辨识的自适应算法
陈坤1,刘毅1, 2,王海清1,宋执环1,李平1
(1.浙江大学 工业控制技术国家重点实验室,工业控制研究所,浙江 杭州 310027;2.浙江工业大学 化工机械设计研究所,浙江 杭州 310032)
Adaptive algorithm for recursive identification of Hammerstein systems
CHEN Kun1, LIU Yi1,2, WANG Hai-qing1, SONG Zhi-huan1, LI Ping1
(1. Institute of Industrial Process Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;
2. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310032, China)
 全文: PDF  HTML
摘要:

采用最小二乘支持向量回归对Hammerstein系统进行辨识缺乏稀疏性,且模型不易递推.提出一种基于输出预报误差的Hammerstein模型自适应稀疏递推辨识算法.根据分块矩阵对模型进行递推运算,基于系统输出预报误差的结果,自适应调整算法的辨识步骤,可以避免递推时可能出现的误差积累问题,有效提高算法的稀疏性和稳定性.仿真结果表明,与常规的递推算法相比,该自适应算法能够在保证辨识精度的情况下,有效稀疏和简化模型,提高算法的鲁棒性和辨识效率,更加符合系统在线辨识的需要.

Abstract:

A new on-line adaptive sparse, recursive identification algorithm of Hammerstein models based on output predictive error was proposed to solve the problems of the least squares support vector regression methods, such as lacking of sparsity and difficult to get a recursive form. The proposed method changes the recursive form, and adaptively chooses the strategy of sparseness/recursion/re-initialization according to the output predictive error. The error accumulation or even divergence problems are avoided and therefore the sparseness and accuracy are improved. The simulation illustrated that compared with the general recursive method, the proposed adaptive algorithm has sparse formulation and simplifies the model while keeping the identification accuracy. Also, the approach is robust and efficient, and it can meet the requirement of online identification.

出版日期: 2010-02-26
:  TP 273  
基金资助:

国家“863”高技术研究发展计划资助项目(2009AA04Z126);国家自然科学基金资助项目(20776128).

通讯作者: 王海清,男,副教授.     E-mail: hqwang@iipc.zju.edu.cn
作者简介: 陈坤(1984-),男,浙江绍兴人,博士生,主要从事核学习建模理论研究.
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

陈坤, 刘毅, 王海清, 等. Hammerstein系统递推辨识的自适应算法[J]. J4, 2010, 44(1): 99-103.

CHEN Kun, LIU Yi, WANG Hai-Qing, et al. Adaptive algorithm for recursive identification of Hammerstein systems. J4, 2010, 44(1): 99-103.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.01.018        http://www.zjujournals.com/eng/CN/Y2010/V44/I1/99

[1] WESTWICK D T, KEARNEY R E. Identification of a Hammerstein model of the stretch reflex EMG using separable least squares [C]∥ Proceedings of the 22nd Annual International Engineering in Medicine and Biology Society. Chicago: IEEE, 2000, 3: 1901-1904.
[2] ESKINAT E, JOHNSON S H, LUYBEN W L. Use of Hammerstein models in identification of nonlinear systems [J]. AIChE Journal, 1991, 37(2): 255-268.
[3] PAWLAK M. On the series expansion approach to the identification [J]. IEEE Transactions on Automatic Control, 1991, 36(6): 736-767.
[4]DEMPSEY E J, WESTWICK D T. Identification of Hammerstein models with cubic spline nonlinearities [J]. IEEE Transactions on Biomedical Engineering, 2004, 51(2): 237-245.
[5] VAN PELT T H, BERNSTEIN D S. Nonlinear systems identification using Hammerstein and nonlinear feedback models with piecewise linear static maps [C]∥ Proceedings of American Control Conference. Chicago: IEEE, 2000, 1(6): 225-229.
[6] AL-DUWAISH H, KARIM M N. A new method for the identification of Hammerstein model [J]. Automatica, 1997, 33(10): 1871-1875.
[7] ESPINOZA M, SUYKENS J A K, DE MOOR B. Partially linear models and least squares support vector machines [C]∥ Proceedings of the 43rd IEEE Conference on Decision and Control. Atlantis, Bahamas: IEEE, 2004.
[8] GOETHALS I, PELCKMANS K, SUYKENS J A K, et al. Identification of MIMO Hammerstein models using least squares support vector machines [J]. Automatica, 2005, 41(7): 1263-1272.
 [9] CHEN K, WANG H Q, SONG Z H. A recursive method of identification of Hammerstein model based on least squares support vector machines [C]∥ Proceedings of the 17th IFAC World Congress. Seoul: Elsevier, 2008: 4993-4998.
[10] LIU Y, YANG D C, WANG H Q, et al. Modeling of fermentation processes using online kernel learning algorithm [C]∥ Proceedings of the 17th IFAC World Congress. Seoul: Elsevier: 2008: 9679-9684.
[11] SUYKENS J A K, VAN GESTEL T, DE BRABANTER J, et al. Least squares support vector machines [M]. Singapore: World Scientific, 2002: 71-116.
[12] VAPNIK V. The nature of statistical learning theory [M]. New York: Springer, 1995: 181-218.
[13] WANG H Q, LI P, GAO F R, et al. Kernel classifier with adaptive structure and fixed memory for process diagnosis [J]. AIChE Journal, 2006, 52(10): 3515-3531.

[1] 程森林,李雷,朱保卫,柴毅. WSN定位中的RSSI概率质心计算方法[J]. J4, 2014, 48(1): 100-104.
[2] 方强, 陈利鹏, 费少华, 梁青霄, 李卫平, 赵金锋. 定位器模型参考自适应控制系统设计[J]. J4, 2013, 47(12): 2234-2242.
[3] 刘丞, 汪昆, 汪雄海. 基于粒子群算法的潮流发电机布局[J]. J4, 2013, 47(12): 2087-2093.
[4] 罗继亮, 王飞,邵辉,赵良煦. 基于约束转换的Petri网最优监控器设计[J]. J4, 2013, 47(11): 2051-2056.
[5] 任雯, 胥布工. 基于FI-SNAPID算法的经编机多速电子送经系统开发[J]. J4, 2013, 47(10): 1712-1721.
[6] 李奇安, 金鑫. 对角CARIMA模型多变量广义预测近似解耦控制[J]. J4, 2013, 47(10): 1764-1769.
[7] 孟德远,陶国良,钱鹏飞,班伟. 气动力伺服系统的自适应鲁棒控制[J]. J4, 2013, 47(9): 1611-1619.
[8] 叶凌云,陈波,张建,宋开臣. 基于最少拍无波纹算法的高精度动态标准源反馈控制[J]. J4, 2013, 47(9): 1554-1558.
[9] 叶凌箭,马修水. 基于软测量技术的化工过程优化控制策略[J]. J4, 2013, 47(7): 1253-1257.
[10] 黄晓烁,何衍,蒋静坪. 基于互联网无刷直流电机传动系统的控制策略[J]. J4, 2013, 47(5): 831-836.
[11] 贺乃宝, 高倩, 徐启华, 姜长生. 基于自适应观测器的飞行器抗干扰控制[J]. J4, 2013, 47(4): 650-655.
[12] 麦志彦,何中杰,汪雄海. 基于主影响因素的城市时用水量预测[J]. J4, 2012, 46(11): 1968-1974.
[13] 朱予辰,冯冬芹,褚健. 基于EPA的块数据流通信调度与控制[J]. J4, 2012, 46(11): 2097-2102.
[14] 刘志鹏, 颜文俊. 预粉磨系统的智能建模与复合控制[J]. J4, 2012, 46(8): 1506-1511.
[15] 朱康武, 顾临怡, 马新军, 胥本涛. 水下运载器多变量鲁棒输出反馈控制方法[J]. J4, 2012, 46(8): 1397-1406.