Evaluation of neural network robust reliability using information-gap theory
Pierce, S.G. and Ben-Haim, Y. and Worden, K. and Manson, G. (2006) Evaluation of neural network robust reliability using information-gap theory. IEEE Transactions on Neural Networks, 17 (6). pp. 1349-1361. ISSN 1045-9227 (http://dx.doi.org/10.1109/TNN.2006.880363)
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A novel technique for the evaluation of neural network robustness against uncertainty using a nonprobabilistic approach is presented. Conventional optimization techniques were employed to train multilayer perceptron (MLP) networks, which were then probed with an uncertainty analysis using an information-gap model to quantify the network response to uncertainty in the input data. It is demonstrated that the best performing network on data with low uncertainty is not in general the optimal network on data with a higher degree of input uncertainty. Using the concepts of information-gap theory, this paper develops a theoretical framework for information-gap uncertainty applied to neural networks, and explores the practical application of the procedure to three sample cases. The first consists of a simple two-dimensional (2-D) classification network operating on a known Gaussian distribution, the second a nine-lass vibration classification problem from an aircraft wing, and the third a two-class example from a database of breast cancer incidence.
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Item type: Article ID code: 11760 Dates: DateEvent2006PublishedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Strathprints Administrator Date deposited: 11 Nov 2011 12:15 Last modified: 08 Apr 2024 16:44 URI: https://strathprints.strath.ac.uk/id/eprint/11760