估计海冰厚度函数的一种辨识方法

提出一种采用海冰和海水温度观测数据来估计海冰厚度的辨识方法, 避免了因使用厚度数据所带来的种种局限性. 首先建立一个拟线性海冰-海水热力学系统, 得到了系统解的存在唯一性; 然后以该系统中描述海冰厚度函数的参数为辨识量, 以系统输出的温度和实际观测温度的偏差为目标泛函, 建立了以目标泛函为最小的参数辨识模型; 最后构造了以半隐式差分格式、遗传算法和Hooke-Jeeves算法相结合的数值算法, 得到了海冰厚度函数, 并对辨识量做了敏感性分析. 结果表明: 这种方法是有效可行的.     

一类非光滑分布参数系统的可辨识性及最优性条件

变压器温度场参数辨识问题是一种分片光滑的分布参数辨识问题,以流速为辨识参数,针对传质传热的一类分布参数系统参数辨识问题,证明了系统最优参数的存在性和控制参数为最优的必要条件,为变压器温度场的数值模拟研究提供了理论基础.     

发汗冷却控制系统的参数辨识问题

研究发汗冷却控制系统中气动加热热流密度的参数辨识问题．证明了该参数辨识的存在及唯一性,给出了参数辨识所满足的充分必要条件,最后,根据得到的充分必要条件,尝试直接构造极小化序列,进而给出该系统参数辨识的算法．     

冰水界面海洋热通量的参数辨识系统

利用参数辨识和冰厚观测研究了固定冰冰底海洋热通量,建立了冰底薄层能量平衡系统,证明了系统解的存在与唯一性.以海洋热通量为辨识参数,观测和计算冰厚差值为目标函数,建立最优辨识模型.利用有界变差函数理论分析最优辨识模型最优解的存在性,通过改进遗传算法求得最优解.根据现场观测的2006-2007年冬季中山站附近固定冰冰厚数据进行了数值模拟,通过2005-2006年数值结果检验表明所建立的冰底薄层能量平衡系统及参数辨识模型是正确有效的.     

基于遗传算法的岩体参数辨识研究

目前,对于岩体流固耦合分析研究已经很多,而耦合分析常常受困于计算参数的取值,因此对两场耦合模型中的计算参数反演分析是非常必要的.根据实测的水头、位移资料,利用遗传算法,建立了等效连续岩体渗流场与应力场耦合计算参数辨识模型.并对某算例在库水位下降情况下,以渗流场与应力场耦合正分析计算结果作为"实测值",进行两场耦合参数辨识分析.从参数辨识的结果来看,验证了所提出的思路、方法以及程序的正确性和可行性.两场耦合计算参数进行反演分.析,对于两场耦合模型的建立和计算结果的可靠性是非常有意义的.     

动力学系统参数辨识问题最优控制解的理论与方法(Ⅰ)--基本概念及确定性系统参数辨识

本文基于动力学系统参数辨识问题最优控制解的概念和确定性动力学系统的最优控制理论,建立了参数辨识研究与最优控制理论的对应关系.将最优控制的数学理论和算法应用于参数辨识问题的研究.依据Hamilton-Jacobi-Bellman (HJB)方程解的理论阐述了动力学系统参数辨识最优控制解的存在唯一性问题,并据此得到了解决确定性系统参数辨识问题的具体算法步骤.     

微生物批式流加发酵脉冲系统的参数辨识及稳定性

针对微生物批式流加发酵生产1,3-丙二醇的非线性脉冲系统,建立敏感参数的优化辨识模型(PDP),论述了模型解的性质、解与参量的关系以及辨识问题最优解的存在性.通过构造算法求得辨识问题最优解,并讨论了新参数下脉冲系统解的稳定性.     

动力学系统参数辨识问题最优控制解的理论与方法（I）—基本概 …

本文基于动力学系统参数辨识问题最优控制解的概念和确定性动力学系统的最优控制理论,建立了参数辨识研究与最优控制理论的对应关系。将最优控制的数学理论和算法应用于参数辨识问题的研究。依据Ｈａｍｉｌｔｏｎ－Ｊａｃｏｂｉ－Ｂｅｌｌｍａｎ（ＨＪＢ）方程解的理论阐述了动力学参数辨识最优控制解的存在唯一性问题,并据此得到了解决确定性系统参数辨识问题的具体算法步骤。     

动力学系统参数辨识问题最优控制解的理论与方法(Ⅱ)——随机系统参数辨识及应用实例

基于文(Ⅰ)(《应用数学和力学》,1998,20(2))的内容和随机最优控制理论,本文首先介绍了随机动力学系统参数辨识问题最优控制解的概念.然后讨论了建立参数辨识问题HJB方程的过程以及参数辨识的算法.最后给出了一个应用实例:解决动力学系统局部非线性参数辨识问题的方法.     

Theory and Algorithm of Optimal Control Solution to Dynamic Systme Parameters Identification (

基于文(Ⅰ)(《应用数学和力学》,1998,20(2))的内容和随机最优控制理论,本文首先介绍了随机动力学系统参数辨识问题最优控制解的概念。然后讨论了建立参数辨识问题HJB方程的过程以及参数辨识的算法。最后给出了一个应用实例解决动力学系统局部非线性参数辨识问题的方法。     

一类弱耦合动力系统的参数识别问题

研究一类弱耦合反应－扩散动力系统的参数识别问题。通过构造上下解，证明了反应－扩散方程组解的存在惟一性；给出了求解参数识别问题的最优化系，从而可以选取适当的梯度法或者共轭梯度法，实现对系统参数的识别。     

Parameter Estimation with the Augmented Lagrangian Method for a Parabolic Equation

In this paper, we investigate the numerical identification of the diffusion parameters in a linear parabolic problem. The identification is formulated as a constrained minimization problem. By using the augmented Lagrangian method, the inverse problem is reduced to a coupled nonlinear algebraic system, which can be solved efficiently with the preconditioned conjugate gradient method. Finally, we present some numerical experiments to show the efficiency of the proposed methods, even for identifying highly discontinuous parameters.This work was partially supported by the Research Council of Norway, Grant NFR-128224/431.     

A note on the iterative solutions of general coupled matrix equation

Recently, Ding and Chen [F. Ding, T. Chen, On iterative solutions of general coupled matrix equations, SIAM J. Control Optim. 44 (2006) 2269-2284] developed a gradient-based iterative method for solving a class of coupled Sylvester matrix equations. The basic idea is to regard the unknown matrices to be solved as parameters of a system to be identified, so that the iterative solutions are obtained by applying hierarchical identification principle. In this note, by considering the coupled Sylvester matrix equation as a linear operator equation we give a natural way to derive this algorithm. We also propose some faster algorithms and present some numerical results.     

Stability estimates for some parameters of a reaction-diffusion equation coupled with an ODE

In this work, we are interested by the monodomain equation which describes the evolution of the cardiac electrical potential and which corresponds to a coupled system involving a reaction–diffusion equation and an ordinary differential equation. We show Lipschitz stability inequalities for the identification of some parameters of the model from measurements on the cardiac potential and the ionic variable.     

Real-coded genetic algorithm for system identification and controller tuning

This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First GA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also, cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in nonlinear pH process. The simulation results show that the GA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system.     

Integrated system modeling and optimization via quasilinearization

System modeling and system optimization are two coupled and strongly related concepts in the modern approach to large-scale systems. Yet, they have been treated as two separate problems in the literature. The identification of system parameters, often referred to as system modeling, is essential in order to obtain an optimal control policy. This work considers the two problems jointly and provides a computational methodology in tackling the integrated problem formulation. This is done by viewing one of the objective functions in the bicriterion problem formulation as a constraint. A computational strategy such as quasilinearization is employed for the solution of the integrated problem. An example problem is introduced, and numerical results using an IBM 360/91 digital computer are presented.The authors are very grateful to Professor C. T. Leondes for his invaluable assistance, guidance, and comments. This research was supported in part by the Air Force Office of Scientific Research, Grant No. 699-67, and in part by the National Science Foundation, Grant No. GK-4086.     

Rapid parameter identification of linear time-delay system from noisy frequency domain data

The technique to identify the system parameters thereof has attracted extensive research interest, since knowing the parameters would enable effective system control strategy and accurate response prediction. In this paper, a novel approach is developed to identify the parameters of the linear time-delay differential system by analyzing the complex system response in the frequency domain. Firstly, the complex frequency response of the time-delay system is expressed as a function of physical parameters and time-delay parameters, forming a typical optimization problem. Subsequently, the sensitivities with respect to the unknown parameters are derived. A novel sensitivity-based algorithm is adopted in the identification procedure. Trust-region constraint is implemented and hence tackled by Tikhonov regularization, which effectively enhances the efficiency of the algorithm. The feasibility and robustness of the identification procedure are evaluated by identifying the parameters of two numerical time-delay systems and an experimental case.     

Robust Solution to Fuzzy Identification Problem with Uncertain Data by Regularization

This study considers the robust identification of the parameters describing a Sugeno type fuzzy inference system with uncertain data. The objective is to minimize the worst-case residual error using a numerically efficient algorithm. The Sugeno type fuzzy systems are linear in consequent parameters but nonlinear in antecedent parameters. The robust consequent parameters identification problem can be formulated as second-order cone programming problem. The optimal solution of this second-order cone problem can be interpreted as solution of a Tikhonov regularization problem with a special choice of regularization parameter which is optimal for robustness (Ghaoui and Lebret (1997). SAIM Journal of Matrix Analysis and Applications 18, 1035–1064). The final regularized nonlinear optimization problem allowing simultaneous identification of antecedent and consequent parameters is solved iteratively using a generalized Gauss–Newton like method. To illustrate the approach, several simulation studies on numerical examples including the modelling of a spectral data function (one-dimensional benchmark example) is provided. The proposed robust fuzzy identification scheme has been applied to approximate the physical fitness of patients with a fuzzy expert system. The identified fuzzy expert system is shown to be capable of capturing the decisions (experiences) of a medical expert.     

Numerical analysis of the coupling of free fluid with a poroelastic material

This paper presents a numerical solution of the coupled system of the time-dependent Stokes and fully dynamic Biot equations. The numerical scheme is based on standard inf-sup stable finite elements in space and the Backward Euler scheme in time. We establish stability of the scheme and derive error estimates for the fully discrete coupled scheme. To handle realistic parameters which may cause nonphysical oscillations in the pore fluid pressure, a heuristic stabilization technique is considered. Numerical errors and convergence rates for smooth problems as well as tests on realistic material parameters are presented.