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基于数据的一类部分未知仿射非线性系统近似解

张国山 王岩浩

张国山, 王岩浩. 基于数据的一类部分未知仿射非线性系统近似解. 自动化学报, 2015, 41(10): 1745-1753. doi: 10.16383/j.aas.2015.c150272
引用本文: 张国山, 王岩浩. 基于数据的一类部分未知仿射非线性系统近似解. 自动化学报, 2015, 41(10): 1745-1753. doi: 10.16383/j.aas.2015.c150272
ZHANG Guo-Shan, WANG Yan-Hao. Data-based Approximate Solution for a Class of Affine Nonlinear Systems with Partially Unknown Functions. ACTA AUTOMATICA SINICA, 2015, 41(10): 1745-1753. doi: 10.16383/j.aas.2015.c150272
Citation: ZHANG Guo-Shan, WANG Yan-Hao. Data-based Approximate Solution for a Class of Affine Nonlinear Systems with Partially Unknown Functions. ACTA AUTOMATICA SINICA, 2015, 41(10): 1745-1753. doi: 10.16383/j.aas.2015.c150272

基于数据的一类部分未知仿射非线性系统近似解

doi: 10.16383/j.aas.2015.c150272
基金项目: 

国家自然科学基金(61074088, 61473202)资助

详细信息
    作者简介:

    王岩浩 天津大学电气与自动化工程学 院硕士研究生. 主要研究方向为机器学 习, 数据驱动控制. E-mail: hhsnh2013@tju.edu.cn

    通讯作者:

    张国山 天津大学电气与自动化工程学 院教授. 主要研究方向为线性与非线性 系统控制, 智能控制与混沌控制及应用. 本文通信作者. E-mail: zhanggs@tju.edu.cn

Data-based Approximate Solution for a Class of Affine Nonlinear Systems with Partially Unknown Functions

Funds: 

Supported by National Natural Science Foundation of China (61074088, 61473202)

  • 摘要: 针对一类部分未知仿射非线性系统无穷区间求解问题,利用在线采样数据,提出了 在线无偏最小二乘支持向量机(Least square support vector machines, LS-SVM)的方法. 首先,通过引入一个参数消除了LS-SVM的偏置项,避免了冗余计算,同时在优化目标中引入权值 函数,对靠近当前时刻的数据样本点赋予更高权重,提高了计算精度; 其次,采用滚动时间窗的方法,实现非线性系统无穷区间求解,并满足求解实时性要求;最后,通过 数值算例仿真验证了本文方法的有效性和优越性.
  • [1] Mohaqeqi M, Kargahi M, Dehghan M. Adaptive scheduling of real-time systems cosupplied by renewable and nonrenewable energy sources. ACM Transactions on Embedded Computing Systems (TECS), 2013, 13(1s): Article No.36
    [2] Yao W, Jiang L, Fang J K, Wen J Y, Cheng S J. Decentralized nonlinear optimal predictive excitation control for multi-machine power systems. International Journal of Electrical Power & Energy Systems, 2014, 55: 620-627
    [3] Qi G Y, Chen Z Q, Yuan Z Z. Adaptive high order differential feedback control for affine nonlinear system. Chaos, Solitons & Fractals, 2008, 37(1): 308-315
    [4] Khan Z H, Gu I Y H. Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling. IEEE Transactions on Cybernetics, 2013, 43(6): 2005-2019
    [5] Ramos J I. Linearization techniques for singular initial-value problems of ordinary differential equations. Applied Mathematics and Computation, 2005, 161(2): 525-542
    [6] Odibat Ζ Μ, Momani S. Application of variational iteration method to nonlinear differential equations of fractional order. International Journal of Nonlinear Sciences and Numerical Simulation, 2006, 7(1): 27-34
    [7] Johnson C. Numerical Solution of Partial Differential Equations by the Finite Element Method. Courier Corporation, 2012.
    [8] Duan J S, Rach R, Baleanu D, Wazwaz A M. A review of the Adomian decomposition method and its applications to fractional differential equations. Communications in Fractional Calculus, 2012, 3(2): 73-99
    [9] Mall S, Chakraverty S. Numerical solution of nonlinear singular initial value problems of Emden-Fowler type using Chebyshev neural network method. Neurocomputing, 2015, 149: 975-982
    [10] Hou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory: the state of the art and perspective. Acta Automatica Sinica, 2009, 35(6): 650-667(侯忠生, 许建新. 数据驱动控制理论及方法的回顾和展望. 自动化学报, 2009, 35(6): 650-667)
    [11] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300
    [12] Zhang G S, Wang S W, Wang Y M, Liu W Q. LS-SVM approximate solution for affine nonlinear systems with partially unknown functions. Journal of Industrial and Management Optimization, 2014, 10(2): 621-636
    [13] Yan Wei-Wu, Chang Jun-Lin, Shao Hui-He. Least square SVM regression method based on sliding time window and its simulation. Journal of Shanghai Jiaotong University, 2004, 38(4): 524-526, 532(阎威武, 常俊林, 邵惠鹤. 基于滚动时间窗的最小二乘支持向量机回归估计方法及仿真. 上海交通大学学报, 2004, 38(4): 524-526, 532)
    [14] Zhou Xin-Ran, Teng Zhao-Sheng. An online sparse LSSVM and its application in system modeling. Journal of Hunan University (Natural Sciences), 2010, 37(4): 37-41(周欣然, 滕召胜. 一种在线稀疏LSSVM及其在系统建模中的应用. 湖南大学学报(自然科学版), 2010, 37(4): 37-41)
    [15] Cai Yan-Ning, Hu Chang-Hua. Dynamic non-bias LS-SVM learning algorithm based on Cholesky factorization. Control and Decision, 2008, 32(12): 1363-1367(蔡艳宁, 胡昌华. 一种基于Cholesky分解的动态无偏LS-SVM学习算法. 控制与决策, 2008, 32(12): 1363-1367)
    [16] Vapnik V. The Nature of Statistical Learning Theory (2nd edition). New York: Springer Science & Business Media, 2000.
    [17] Lázaro M, Santamaría I, Pérez-Cruz F, Artés-Rodríguez A. Support vector regression for the simultaneous learning of a multivariate function and its derivatives. Neurocomputing, 2005, 69(1-3): 42-61
    [18] Mehrkanoon S, Falck T, Suykens J A K. Approximate solutions to ordinary differential equations using least squares support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(9): 1356-1367
    [19] Cawley G C, Talbot N L C. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks, 2004, 17(10): 1467-1475
    [20] El-Tawil M A, Bahnasawi A A, Abdel-Naby A. Solving Riccati differential equation using Adomian's decomposition method. Applied Mathematics and Computation, 2004, 157(2): 503-514
    [21] Lagaris I E, Likas A, Fotiadis D I. Artificial neural networks for solving ordinary and partial differential equations. IEEE Transactions on Neural Networks, 1998, 9(5): 987-1000
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出版历程
  • 收稿日期:  2015-05-11
  • 修回日期:  2015-07-27
  • 刊出日期:  2015-10-20

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