Please use this identifier to cite or link to this item: http://dlib.scu.ac.ir/handle/Hannan/165776
Title: Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals
Authors: Wenhua Han;Jun Xu;Mengchu Zhou;Guiyun Tian;Ping Wang;Xiaohui Shen;Edwin Hou
subject: Cuckoo search|particle filter|inversing problem|magnetic flux leakage
Year: 2016
Publisher: IEEE
Abstract: Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter's robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.
URI: http://localhost/handle/Hannan/165776
ISSN: 0018-9464
1941-0069
volume: 52
issue: 4
More Information: 1
11
Appears in Collections:2016

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Title: Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals
Authors: Wenhua Han;Jun Xu;Mengchu Zhou;Guiyun Tian;Ping Wang;Xiaohui Shen;Edwin Hou
subject: Cuckoo search|particle filter|inversing problem|magnetic flux leakage
Year: 2016
Publisher: IEEE
Abstract: Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter's robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.
URI: http://localhost/handle/Hannan/165776
ISSN: 0018-9464
1941-0069
volume: 52
issue: 4
More Information: 1
11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7321028.pdf348.02 kBAdobe PDFThumbnail
Preview File
Title: Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals
Authors: Wenhua Han;Jun Xu;Mengchu Zhou;Guiyun Tian;Ping Wang;Xiaohui Shen;Edwin Hou
subject: Cuckoo search|particle filter|inversing problem|magnetic flux leakage
Year: 2016
Publisher: IEEE
Abstract: Accurate and timely prediction of defect dimensions from magnetic flux leakage signals requires one to solve an inverse problem efficiently. This paper proposes a new inversing approach to such a problem. It combines cuckoo search (CS) and particle filter (PF) to estimate the defect profile from measured signals and adopts a radial-basis function neural network as a forward model as well as the observation equation in PF. As one of the latest nature-inspired heuristic optimization algorithms, CS can solve high-dimensional optimization problems. As an effective estimator for a nonlinear filtering problem, PF is applied to the proposed inversing approach in order to improve the latter's robustness to the noise. The resulting algorithm enjoys the advantages of both CS and PF where CS produces the optimized state sequence for PF while PF processes the state sequence and estimates the desired profile. The simulation and experimental results have demonstrated that the proposed approach is significantly better than the inversing approach based on CS alone in a noisy environment.
URI: http://localhost/handle/Hannan/165776
ISSN: 0018-9464
1941-0069
volume: 52
issue: 4
More Information: 1
11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7321028.pdf348.02 kBAdobe PDFThumbnail
Preview File