Combined Parametric-Nonparametric Identification of by Grzegorz Mzyk

By Grzegorz Mzyk

This e-book considers an issue of block-oriented nonlinear dynamic approach identity within the presence of random disturbances. This category of platforms contains numerous interconnections of linear dynamic blocks and static nonlinear parts, e.g., Hammerstein process, Wiener procedure, Wiener-Hammerstein ("sandwich") process and additive NARMAX structures with suggestions. Interconnecting indications aren't obtainable for dimension. The mixed parametric-nonparametric algorithms, proposed within the ebook, should be chosen dependently at the previous wisdom of the process and signs. such a lot of them are in accordance with the decomposition of the complicated process id job into easier neighborhood sub-problems through the use of non-parametric (kernel or orthogonal) regression estimation. within the parametric degree, the generalized least squares or the instrumental variables process is often utilized to deal with correlated excitations. restrict houses of the algorithms were proven analytically and illustrated in basic experiments.

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Extra info for Combined Parametric-Nonparametric Identification of Block-Oriented Systems

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G. 54)). 29) we can show that the following theorems hold. 5. 32) does exist and is not singular =0 → 0. Proof. 8. 6. 33) as N, M → ∞, provided that N M −τ → 0. 34) Proof. 4 with obvious substitutions, and hence the proof is here omitted. 32) in general we have 0 < τ < 1/2, hence to fulfil the conditions (a’) and (b’) far more input-output data, M , must be used in Stage 1 for nonparametric estimation of interactions {wk } than the inputs (IV ) and instruments, N , for computation of the estimate θN,M in Stage 2.

In the proposed approach the identification is performed in two stages. First, exploiting a nonparametric regression estimation technique, the unmeasurable inner signal {wk } is estimated from the measurement data (uk , yk ). Then, the least squares method is used to the independent estimation of the two subsystems parameters using, respectively, the pairs (uk , wk ) and (wk , yk ) where {wk } is the estimate of the interaction sequence obtained by a nonparametric method. As compared to the parametric identification techniques developed to date, the potential advantages of the approach are that: G.

E. 8. 50) where RM (u) is a nonparametric estimate of the regression function R(u) = E[yk |uk = u]. 51) n=1 getting the solution cN0 ,M . Take the computed cN0 ,M as the estimate of c∗ . 10 along with the fact that by assumption μ(0) = 0 we further get that μ(u, c∗ ) = R(u) − R(0). 9. e. e. factually we can estimate μ(u) for each timelag l between output and input, for which γl = 0. Certainly, the best choice of l is for the |γl | being maximal. For further discussion we refer to [91] and [92].

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