Estimation of random parameters by the method of prior probability maximum
type: Item
instance of: scholarly article
title: Estimation of random parameters by the method of prior probability maximum
zbMATH Open document ID: 0847.93057
author: V. K. Zavadskij
published in: Automation and Remote Control
publication date: 1996-06-12T00:00:00Z
review text: A method for random parameters estimation is considered. An approach is applied to solve the problem of constructing an a posteriori density function which is used to form an estimation criterion. The basic computational problems arise from the complexity of the system models and they are more complicated in the case of nonlinear system identification. The author proposes a simpler way to obtain the most probable estimates in non-Gaussian cases. The paper contains results which generalize the least-squares method and the discrete Kalman-Bucy filter, to the case of correlated perturbations and interferences. The paper will be of interest for the specialists and engineers, working on nonlinear system state estimation and identifiction.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 885270
zbMATH Keywords: nonlinear system identification
reviewed by: Tzvetan Semerdjiev
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 885270