Volterra filter identification using penalized least squares
نویسنده
چکیده
Volterra lters have been applied to many nonlinear system identiication problems. However, obtaining good lter estimates from short and/or noisy data records is a diicult task. We propose a penalized least squares estimation algorithm and derive appropriate penalizing functionals for Volterra lters. An example demonstrates that penalized least squares estimation can provide much more accurate lter estimates than ordinary least squares estimation.
منابع مشابه
Identification of Svd-parafac Based Third-order Volterra Models Using an Arls Algorithm
A broad class of nonlinear systems can be modeled by the Volterra series representation. However, the practical use of such a representation is often limited due to the large number of parameters associated with the Volterra filter structure. This paper is concerned with the problem of identification of third-order Volterra systems. The SVD technique is used to represent the quadratic Volterra ...
متن کاملMicrosoft Word - variantwseas_2007_1.rtf
The Volterra series have been successfully and widely applied as a nonlinear system modeling technique. Considered as a prototype, the second order Volterra filter (FV2) has an increased complexity in comparison with a linear filter. The filter based on the multi memory decomposition (MMD) structure represents a good approximation of the FV2 and significantly reduces the number of the filter op...
متن کاملNonlinear Systems Identification Using the Volterra Model
Nonlinear adaptive filtering techniques are widely used for the nonlinearities identification in manny applications. This paper investigates the performances of the Volterra estimator by considering a nonlinear system identification application. The Volterra estimator parameters are compared with those of a linear estimator. For the nonlinear estimator, based on a second order RLS Volterra filt...
متن کاملMicrosoft Word - 508-182_budura.rtf
Nonlinear adaptive filtering techniques, based on the Volterra model, are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order LMS Volterra filter. A third order nonlinear system with memory is identified using the new LMS algorithm implementation for the Volterra kernels estimation. The accuracy of the proposed algo...
متن کاملRecursive ℓ1, ∞ Group Lasso
We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal `1,∞-penalized recursive least squares (RLS) predictor. Each update minimizes a convex but nondifferentiable function optimization ...
متن کامل