Error and variance bounds on sigmoidal neurons with weight and input errors

Lovell, D. R., Bartlett, P., & Downs, T. (1992) Error and variance bounds on sigmoidal neurons with weight and input errors. Electronics Letters, 28(8), pp. 760-762.

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Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.

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3 citations in Web of Science®

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ID Code: 79893
Item Type: Journal Article
Refereed: Yes
Additional Information: Cited By :1
Export Date: 6 January 2015
Keywords: Mathematical Techniques--Error Analysis, Statistical Methods, Feedforward neural networks, Sigmoidal neurons, Variance bounds, Neural Networks
DOI: 10.1049/el:19920480
ISSN: 0013-5194 (ISSN)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: IEEE
Deposited On: 07 Jan 2015 05:17
Last Modified: 25 Mar 2015 02:44

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