Pierce, S.G. and Worden, K. and Manson, G. (2005) Considerations for practical neural network application to a damage detection problem. Key Engineering Materials, 293. pp. 151-158. ISSN 1013-9826Full text not available in this repository. (Request a copy from the Strathclyde author)
The application of a multilayer perceptron (MLP) neural network to a damage location problem on a GNAT aircraft wing is considered. The problems associated with effective network training and evaluation are discussed, focussing on ensuring good generalisation performance of the network to the classification of new data. Both conventional Maximum Likelihood and Bayesian Evidence based training techniques are considered and a simple thresholding technique is presented to aid in the rejection of poorly regularised network structures. Examples are presented for an artificial simple 2 class problem (drawn from a Gaussian distribution) and a real 9 class problem on the GNAT aircraft wing.
|Keywords:||damage detection, neural networks, regularisation, generalisation, thresholding, power systems, Electrical engineering. Electronics Nuclear engineering, Mechanics of Materials, Materials Science(all), Mechanical Engineering|
|Subjects:||Technology > Electrical engineering. Electronics Nuclear engineering|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||14 Oct 2008|
|Last modified:||22 Mar 2017 09:42|