Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

By Meihang Li, Ximei Liu. 2020

191 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/13bb5d96005889acd408875aba8a3f58a54eef2d

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In Artificial Intelligence

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, unknown): “Then, Equation (5) can be rewritten as y(k) = φ1 (k, α)γ + γ TF(k, α)b + φ2 (k, α)h + v(k) = φT(k, α)θ + v(k).”
Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems (2021, unknown): “Thus we have the recursive least squares algorithm: ?̂?z(t) = ?̂?z(t − 1) + P1(t)ξz(t)[s1(t) − ξz (t)?̂?z(t − 1)], (13) P−1 1 (t) = P −1 1 (t − 1) + ξz(t)ξ T z (t), P1(0) = p0Imz , (14) ?̂?xq(t) = ?̂?xq(t − 1) + P2(t)ξxq(t)[s2(t) − ξxq(t)?̂?xq(t − 1)], (15) P−1 2 (t) = P −1 2 (t − 1) + ξxq(t)ξ T xq(”
Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems (2020, unknown): “Form the information vector ?̂?(t) using (30) and form the information matrix ?̂?(p, t) using (29).”
Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems (2020, unknown): “Replacing t with th in Equations (7) and (8), we have the following dual-rate identification model: y(th) = φT(th)τ + ψT(th)ρ + v(th) (9) = φT(th)θ + v(th), (10)”
Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory (2021, methodology): “Compute L1(t) and P1(t) using (30)–(31), and update the parameter estimation vector ?̂?(t) using (29).”