A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems

By Jianqiang Pan, Xiao Jiang, Xiangkui Wan, Wenfang Ding. 2017

287 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Filtering‐based recursive least squares estimation approaches for multivariate equation‐error systems by using the multiinnovation theory (2021, unknown): “Construct ?̂?y(t) and ?̂?s(t) using (34)–(35), and compute ŷf(t) and ?̂?f(t) using (32)–(33).”
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 Y(k) by (45) and the stacked information matrices ?̂?l(k), ?̂?1,l(k), ?̂?2,l(k), and ?̂? l(k) by (46)–(49), select a large μ1,l(k) and μ2,l(k) according to (41) and (43).”
Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems (2021, unknown): “(24) Then Equations (13)–(18) can be rewritten as ?̂?z(t) = ?̂?z(t − 1) + L1(t)[s1(t) − ξz (t)?̂?z(t − 1)], (25) L1(t) = P1(t − 1)ξz(t) 1 + ξz (t)P1(t − 1)ξz(t) , P1(0) = p0Imz , (26) P1(t) = [Imz − L1(t)ξ T z (t)]P1(t − 1), (27) ?̂?xq(t) = ?̂?xq(t − 1) + L2(t)[s2(t) − ξxq(t)?̂?xq(t − 1)], (28)”
Parameter estimation for pseudo-linear systems using the auxiliary model and the decomposition technique (2017, methodology): “The decomposition methods can be applied to multivariable systems [20], time-varying systems [21], non-linear systems [22] and other fields [23, 24].”