Abstract
The Cesaro means of orthogonal series are applied to construct general regression neural networks. Sufficient conditions for convergence in probability are given assuming nonstationary noise. An experiment with syntetic data is described. © 2012 Springer-Verlag.
Cite
CITATION STYLE
APA
Duda, P., & Zurada, J. M. (2012). On the Cesaro orthogonal series-type kernel probabilistic neural networks handling non-stationary noise. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7203 LNCS, pp. 435–442). https://doi.org/10.1007/978-3-642-31464-3_44
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
Already have an account? Sign in
Sign up for free