On convergence proofs in system identification -- a general principle using ideas from learning theory

DOI10.1016/S0167-6911(97)00134-5zbMath0902.93017OpenAlexW2075469705MaRDI QIDQ1128973

S. H. Smith

Publication date: 13 August 1998

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-6911(97)00134-5

zbMATH Keywords

convergencesystem identificationnonlinear systemslearning theorystochastic processesparameterizationstructural identificationmodel sets

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) System identification (93B30)

Cites Work

• Unnamed Item
• Unnamed Item
• On covariance function tests used in system identification
• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• A theory of the learnable
• On a class of mixing processes
• Convergence analysis of parametric identification methods
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Convergence of stochastic processes

This page was built for publication: On convergence proofs in system identification -- a general principle using ideas from learning theory