Abstract
Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximation to the performance degradation of a MLP affected by perturbations. In the present paper, the expression of MSS for Radial Basis Function Neural Networks affected by additive noise in their inputs is obtained. This expression is experimentally validated, allowing us to propose MSS as an effective measurement of noise immunity of RBFNs. © Springer-Verlag Berlin Heidelberg 2001.
Cite
CITATION STYLE
Bernier, J. L., González, J., Cañas, A., & Ortega, J. (2001). Assessing the noise immunity of radial basis function neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2085 LNCS, pp. 136–143). Springer Verlag. https://doi.org/10.1007/3-540-45723-2_16
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
