The parameter estimation algorithms based on the dynamical response measurement data

• Published in: Advances in mechanical engineering Vol. 9; no. 11; p. 168781401773000
• Main Author: Xu, Ling
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-11-2017; Sage Publications Ltd; SAGE Publishing
• Subjects: Accuracy; Algorithms; Computer simulation; Construction costs; Decomposition; Dynamical systems; Estimates; Experiments; Fault diagnosis; Innovations; Kalman filters; Laplace transforms; Methods; Neural networks; Parameter estimation; Parameter identification; Parameters; Process controls; Signal processing; rolling optimization; multi-innovation; Parameter estimation; system response; nonlinear optimization
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814017730003

This article studies the parameter estimation to the system response from the discrete measurement data. By constructing the dynamical rolling cost functions and using the nonlinear optimization, the gradient identification method is presented for estimating the parameters of the sine response signal with double frequency. In order to overcome the difficulty for determining the step size and deduce the influence of noises, the stochastic gradient identification method is derived to estimate the signal parameters. For the purpose of improving the accuracy, a multi-innovation stochastic gradient parameter estimation algorithm is presented using the moving window data. Finally, the simulation examples are provided to test the algorithm performance.