Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

We propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transfo...

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Main Authors: ChunPing Ren, NengJian Wang, ChunSheng Liu
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2017/9125734
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spelling doaj-337866eb14b549a8888055fb8054056e2020-11-24T22:58:13ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/91257349125734Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate GradientChunPing Ren0NengJian Wang1ChunSheng Liu2College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaSchool of Mechanical Engineering, Heilongjiang University of Science and Technology, Harbin 150022, ChinaWe propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transformed into an unconstrained optimization problem, and a novel conjugate gradient (NCG) method was applied to solve the objective function, which was abbreviated as IME-NCG algorithm. The result of IME-NCG algorithm is compared with that of ME, ME-CG, ME-NCG, and IME-CG algorithm; it is found that IME-NCG algorithm is available for identifying the random dynamic force due to smaller root mean-square-error (RMSE), lower restoration time, and fewer iterative steps. Example of engineering application shows that L-curve method is introduced which is better than Generalized Cross Validation (GCV) method and is applied to select regularization parameter; thus the proposed algorithm can be helpful to alleviate the ill-conditioned problem in identification of dynamic force and to acquire an optimal solution of inverse problem in practical engineering.http://dx.doi.org/10.1155/2017/9125734
collection DOAJ
language English
format Article
sources DOAJ
author ChunPing Ren
NengJian Wang
ChunSheng Liu
spellingShingle ChunPing Ren
NengJian Wang
ChunSheng Liu
Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
Mathematical Problems in Engineering
author_facet ChunPing Ren
NengJian Wang
ChunSheng Liu
author_sort ChunPing Ren
title Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
title_short Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
title_full Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
title_fullStr Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
title_full_unstemmed Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient
title_sort identification of random dynamic force using an improved maximum entropy regularization combined with a novel conjugate gradient
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2017-01-01
description We propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transformed into an unconstrained optimization problem, and a novel conjugate gradient (NCG) method was applied to solve the objective function, which was abbreviated as IME-NCG algorithm. The result of IME-NCG algorithm is compared with that of ME, ME-CG, ME-NCG, and IME-CG algorithm; it is found that IME-NCG algorithm is available for identifying the random dynamic force due to smaller root mean-square-error (RMSE), lower restoration time, and fewer iterative steps. Example of engineering application shows that L-curve method is introduced which is better than Generalized Cross Validation (GCV) method and is applied to select regularization parameter; thus the proposed algorithm can be helpful to alleviate the ill-conditioned problem in identification of dynamic force and to acquire an optimal solution of inverse problem in practical engineering.
url http://dx.doi.org/10.1155/2017/9125734
work_keys_str_mv AT chunpingren identificationofrandomdynamicforceusinganimprovedmaximumentropyregularizationcombinedwithanovelconjugategradient
AT nengjianwang identificationofrandomdynamicforceusinganimprovedmaximumentropyregularizationcombinedwithanovelconjugategradient
AT chunshengliu identificationofrandomdynamicforceusinganimprovedmaximumentropyregularizationcombinedwithanovelconjugategradient
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