Abstract:
In this paper, we present a new algorithm for unconstrained optimization problem with the form of sum of squares minimization that is produced in the procedure of model p...Show MoreMetadata
Abstract:
In this paper, we present a new algorithm for unconstrained optimization problem with the form of sum of squares minimization that is produced in the procedure of model parameter estimation for nonlinear systems. The new algorithm is composed of conventional BFGS and analytical exact line search where the line search step is calculated by an analytical equation in which the second derivative matrix called Hessian matrix is approximated by the product of Jacobian matrices of objective function. Two case studies show that the new algorithm exhibits excellent convergence performance in terms of computation time and initial values requirement.
Published in: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
Date of Conference: 10-13 October 2004
Date Added to IEEE Xplore: 07 March 2005
Print ISBN:0-7803-8566-7
Print ISSN: 1062-922X
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Algorithm For Problem ,
- Unconstrained Optimization Problem ,
- Algorithm For Optimization Problems ,
- Parameter Estimates ,
- Objective Function ,
- Computation Time ,
- Nonlinear Systems ,
- Second Derivative ,
- Model Parameter Estimates ,
- Hessian Matrix ,
- Convergence Performance ,
- Line Search ,
- Exact Search ,
- Parameter Estimation Procedure ,
- Exact Line ,
- Performance Of Algorithm ,
- Weight Matrix ,
- Square Deviation ,
- Sum Of Squares ,
- Newton Method ,
- Marquardt Method ,
- Levenberg-Marquardt Algorithm ,
- Polynomial Interpolation ,
- Descent Direction ,
- Parameter Estimation Problem
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Algorithm For Problem ,
- Unconstrained Optimization Problem ,
- Algorithm For Optimization Problems ,
- Parameter Estimates ,
- Objective Function ,
- Computation Time ,
- Nonlinear Systems ,
- Second Derivative ,
- Model Parameter Estimates ,
- Hessian Matrix ,
- Convergence Performance ,
- Line Search ,
- Exact Search ,
- Parameter Estimation Procedure ,
- Exact Line ,
- Performance Of Algorithm ,
- Weight Matrix ,
- Square Deviation ,
- Sum Of Squares ,
- Newton Method ,
- Marquardt Method ,
- Levenberg-Marquardt Algorithm ,
- Polynomial Interpolation ,
- Descent Direction ,
- Parameter Estimation Problem
