NDLI: Application of Improved BCC Algorithm and RBFNN in Identification of Defect Parameters

Content Provider	IEEE Xplore Digital Library
Author	Kou Wei Sun Feng-rui Yang Li Chen Lin-gen
Copyright Year	2010
Abstract	The identification of defect parameters in thermal non-destructive test and evaluation (TNDT/E) was considered as a kind of inverse heat transfer problem (IHTP) and a kind of structure design optimization problem, and the design results should meet the surface temperature profile of the apparatus with defects. An improved bacterial colony chemo taxis (IBCC) optimization algorithm and a radial basis function neural network (RBFNN) are applied to the identification of defects parameters. The RBFNN is a precise and convenient surrogate model for the time costly finite element computation, which obtains the surface temperature with different defect parameters. The IBCC optimization algorithm is derivatively-free, and the convergence speed is fast. This method is applied to a simple verification case and the result is acceptable. The algorithm is also compared with the particle swarm optimization (PSO) algorithm, and the IBCC algorithm can access the optimum with faster speed.
Starting Page	160
Ending Page	164
File Size	267868
Page Count	5
File Format	PDF
ISBN	9781424488919
DOI	10.1109/ICGEC.2010.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature measurement Inverse heat transfer problem Microorganisms Shape Computational modeling Artificial neural networks Defect Identification Bacterial colony chemotaxis algorithm Radial basis function neural network Optimization Heat transfer
Content Type	Text
Resource Type	Article

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