Noise sensitivity of static neural network classifiers

Steven D. Beck; Joydeep Ghosh

doi:10.1117/12.140061

16 September 1992 Noise sensitivity of static neural network classifiers

Steven D. Beck, Joydeep Ghosh

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140061
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

A variety of artificial neural networks are evaluated for their classification abilities under noisy inputs. These networks include feedforward networks, localized basis function networks, and exemplar classifiers. The performance of radial basis function classifiers deteriorate rapidly in the presence of noise, but elliptical basis variants are able to adapt to extraneous input components quite robustly. For feedforward networks, selective pruning of weights based on an `optimal brain damage' approach helps in noise-tolerant classification. Results from a radar classification problem are presented.

Citation Download Citation

Steven D. Beck and Joydeep Ghosh "Noise sensitivity of static neural network classifiers", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140061

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