The parameter estimation algorithms based on the dynamical response measurement data
By Ling Xu. 2017
178 citations recorded by the source corpus.
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.
Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/0b19f829b473ac1a77f294c0321d6741f89b68c3
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In Civil and Structural Engineering
In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems (2021, background): “Collect the observation data s(t) and u(t), construct the information vector ξz(t) by (47), ?̂?xq(t) by (48) and ?̂?pg(t) by (51).”
- Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation (2021, methodology): “The steps of computing ?̂?l in the KT-AM-GI algorithm in (18)–(30) are summarized in the following.”