Performance analysis of the generalised projection identification for time-varying systems
By Feng Ding, Ling Xu, Quanmin Zhu. 2016
136 citations recorded by the source corpus.
The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.
Subfields: Civil and Structural Engineering, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/3d3472a5e930de00a80775839a284aa1f716ab90
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In Civil and Structural Engineering
In Control and Systems Engineering
Citations in the corpus
- Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation (2021, background): “Construct the stacked output vector and the stacked information matrix as follows: Y(L) ∶= [y(1), y(2), .”
- Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems (2021, unknown): “From (2), (3), (5), and (6), we can compute the outputs ŵ(t), l̂(t), and v̂(t) of the auxiliary models by ŵ(t) = [1 − X̂(t, e)]ŵ(t) + Q̂(t, e) ̂̄ u(t), (58)”