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Parameter Identification Problems (parameter + identification_problem)
Selected AbstractsOptimal state filtering and parameter identification for linear systemsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2008Michael Basin Abstract This paper presents the optimal filtering and parameter identification problem for linear stochastic systems with unknown multiplicative and additive parameters over linear observations, where unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained optimal filter for the extended state vector also serves as the optimal identifier for the unknown parameters. Performance of the designed optimal state filter and parameter identifier is verified for both stable and unstable linear uncertain systems. Copyright © 2007 John Wiley & Sons, Ltd. [source] Parameter identification for leaky aquifers using global optimization methodsHYDROLOGICAL PROCESSES, Issue 7 2007Hund-Der Yeh Abstract In the past, graphical or computer methods were usually employed to determine the aquifer parameters of the observed data obtained from field pumping tests. Since we employed the computer methods to determine the aquifer parameters, an analytical aquifer model was required to estimate the predicted drawdown. Following this, the gradient-type approach was used to solve the nonlinear least-squares equations to obtain the aquifer parameters. This paper proposes a novel approach based on a drawdown model and a global optimization method of simulated annealing (SA) or a genetic algorithm (GA) to determine the best-fit aquifer parameters for leaky aquifer systems. The aquifer parameters obtained from SA and the GA almost agree with those obtained from the extended Kalman filter and gradient-type method. Moreover, all results indicate that the SA and GA are robust and yield consistent results when dealing with the parameter identification problems. Copyright © 2006 John Wiley & Sons, Ltd. [source] High-resolution, monotone solution of the adjoint shallow-water equationsINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 2 2002Brett F. Sanders Abstract A monotone, second-order accurate numerical scheme is presented for solving the differential form of the adjoint shallow-water equations in generalized two-dimensional coordinates. Fluctuation-splitting is utilized to achieve a high-resolution solution of the equations in primitive form. One-step and two-step schemes are presented and shown to achieve solutions of similarly high accuracy in one dimension. However, the two-step method is shown to yield more accurate solutions to problems in which unsteady wave speeds are present. In two dimensions, the two-step scheme is tested in the context of two parameter identification problems, and it is shown to accurately transmit the information needed to identify unknown forcing parameters based on measurements of the system response. The first problem involves the identification of an upstream flood hydrograph based on downstream depth measurements. The second problem involves the identification of a long wave state in the far-field based on near-field depth measurements. Copyright © 2002 John Wiley & Sons, Ltd. [source] dsoa: The implementation of a dynamic system optimization algorithmOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 3 2010Brian C. Fabien Abstract This paper describes the ANSI C/C++ computer program dsoa, which implements an algorithm for the approximate solution of dynamics system optimization problems. The algorithm is a direct method that can be applied to the optimization of dynamic systems described by index-1 differential-algebraic equations (DAEs). The types of problems considered include optimal control problems and parameter identification problems. The numerical techniques are employed to transform the dynamic system optimization problem into a parameter optimization problem by: (i) parameterizing the control input as piecewise constant on a fixed mesh, and (ii) approximating the DAEs using a linearly implicit Runge-Kutta method. The resultant nonlinear programming (NLP) problem is solved via a sequential quadratic programming technique. The program dsoa is evaluated using 83 nontrivial optimal control problems that have appeared in the literature. Here we compare the performance of the algorithm using two different NLP problem solvers, and two techniques for computing the derivatives of the functions that define the problem. Copyright © 2009 John Wiley & Sons, Ltd. [source] Direct optimization of dynamic systems described by differential-algebraic equationsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2008Brian C. Fabien Abstract This paper presents a method for the optimization of dynamic systems described by index-1 differential-algebraic equations (DAE). The class of problems addressed include optimal control problems and parameter identification problems. Here, the controls are parameterized using piecewise constant inputs on a grid in the time interval of interest. In addition, the DAE are approximated using a Rosenbrock,Wanner (ROW) method. In this way the infinite-dimensional optimal control problem is transformed into a finite-dimensional nonlinear programming problem (NLP). The NLP is solved using a sequential quadratic programming (QP) technique that minimizes the L, exact penalty function, using only strictly convex QP subproblems. This paper shows that the ROW method discretization of the DAE leads to (i) a relatively small NLP problem and (ii) an efficient technique for evaluating the function, constraints and gradients associated with the NLP problem. This paper also investigates a state mesh refinement technique that ensures a sufficiently accurate representation of the optimal state trajectory. Two nontrivial examples are used to illustrate the effectiveness of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||