By Topic

Defect reconstruction from MFL signals using improved genetic local search algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wenhua Han ; Inst. of Autom. Detection, Shanghai Jiao Tong Univ. ; Peiwen Que

This paper presents an improved GLSA (IGLSA) by incorporating the simulated annealing technique into the perturbation process of the genetic local search (GLSA), and proposes an IGLSA-based inverse algorithm for 2-D defect reconstruction from the magnetic flux leakage (MFL) signals. In the algorithm, radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem. Experiments are presented to show the performance of the IGLSA-based inverse algorithm and to compare it with the canonical-genetic-algorithm based (CGA-based) inverse algorithm and the GLSA-based inverse algorithm, respectively. The results demonstrate that IGLSA-based inverse algorithm is more accurate and is robust to the noise

Published in:

Industrial Technology, 2005. ICIT 2005. IEEE International Conference on

Date of Conference:

14-17 Dec. 2005