Orthonormal basis of the octonionic analytic functions

Modelling with Orthonormal Basis Functions

System identification with generalized orthonormal basis functions

A least-squares identification method is studied that estimates a finite number of expansion coefficients in the series expansion of a transfer function, where the expansion is in terms of recently introduced generalized basis functions. The basis functions are orthogonal in 8$, and generalize the pulse, Laguerre and Kautz bases. One of their important properties is that, when chosen properly, ...

Mimo System Identification Using Orthonormal Basis Functions

Abstract There has re ently been interest in the use of orthonormal bases for the purposes of SISO system identi ation. Con urrently, but separately, there has also been vigorous work on estimation of MIMO systems by omputationally heap and reliable s hemes. These latter ideas have olle tively be ome known as `4SID' methods. This paper is a ontribution overlapping these two s hools of thought b...

Asymptotically optimal orthonormal basis functions for LPV system identification

A global model structure is developed for parametrization and identification of a general class of Linear Parameter-Varying (LPV) systems. By using a fixed orthonormal basis function (OBF) structure, a linearly parametrized model structure follows for which the coefficients are dependent on a scheduling signal. An optimal set of OBFs for this model structure is selected on the basis of local li...

Filters Parametrized by Orthonormal Basis Functions for Active Noise Control

Parametrization of filters on the basis of orthonormal basis functions have been widely used in system identification and adaptive signal processing. The main advantage of using orthonormal basis functions for a filter parametrization lies in the possibility of incorporating prior knowledge of the system dynamics into the identification process and adaptive signal process. As a result, a more a...