2010Guangdian gongchengRequires access

Application of Tikhonov Regularization Method in Zernike Polynomials Fitting

Kai Fang

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Abstract

To acquire coefficients of Zernike polynomials is a discrete ill-posedness problem.Common methods,such as Least Squares Error method,Cram-Schmidt orthogonalization method and Householder transformation method,can not obtain a stable numerical solution.The instability of ill-posedness problem is analyzed and a Tikhonov regularization method is introduced to solve ill-posed problem.The L curve criterion is used to determine regularization parameter.Simulation result shows that Tikhonov regularization method is stable and effective for solving ill-posedness problem.

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What this paper is about

To acquire coefficients of Zernike polynomials is a discrete ill-posedness problem.Common methods,such as Least Squares Error method,Cram-Schmidt orthogonalization method and Householder transformation method,can not obtain a stable numerical solution.The instability of ill-posedness problem is analyzed and a Tikhonov regularization method is introduced to solve ill-posed problem.The L curve criterion is used to determine regularization parameter.Simulation result shows that Tikhonov regularization method is stable and effective for solving ill-posedness problem.

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Available abstract

To acquire coefficients of Zernike polynomials is a discrete ill-posedness problem.Common methods,such as Least Squares Error method,Cram-Schmidt orthogonalization method and Householder transformation method,can not obtain a stable numerical solution.The instability of ill-posedness problem is analyzed and a Tikhonov regularization method is introduced to solve ill-posed problem.The L curve criterion is used to determine regularization parameter.Simulation result shows that Tikhonov regularization method is stable and effective for solving ill-posedness problem.

Key concepts: Tikhonov regularization, Orthogonalization, Regularization (linguistics), Zernike polynomials, Mathematics, Backus–Gilbert method, Regularization perspectives on support vector machines, Applied mathematics

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