Linear and nonlinear associative memories for parameter estimation

DOI10.1016/0020-0255(92)90033-5zbMath0796.93055OpenAlexW2133572203MaRDI QIDQ1183172

S. Singh

Publication date: 28 June 1992

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0255(92)90033-5

zbMATH Keywords

least squaresassociative memoryparameter estimates

Mathematics Subject Classification ID

Linear systems in control theory (93C05) Control/observation systems governed by ordinary differential equations (93C15)

Related Items (6)

Obtaining initial parameter estimates for chaotic dynamical systems using linear associative memories ⋮ An adaptive learning approach to the identification of structural and mechanical systems ⋮ Smooth fitting with a method for determining the regularization parameter under the genetic programming algorithm ⋮ The correlation memory matrix for parameter estimation ⋮ Weighted linear associative memory approach to nonlinear parameter estimation ⋮ Function approximations by superimposing genetic programming trees: with applications to engineering problems

Cites Work

• Self-organization and associative memory.
• On optimal nonlinear associative recall
• Multilayer feedforward networks are universal approximators
• Regression and the Moore-Penrose pseudoinverse
• Learning representations by back-propagating errors
• Introduction to variance estimation

This page was built for publication: Linear and nonlinear associative memories for parameter estimation