title: Recursive least squares estimation of nonlinear multiple-input systems using orthonormal function expansions.
abstract: We present a computational scheme to obtain adaptive non-linear, multiple-input models of the Volterra-Wiener class, by utilizing function expansions of the Volterra kernels in a recursive least-squares formulation. Function expansions have been proven successful in linear and nonlinear systems identification as they result in a significant reduction of the required free parameters, which is a major limiting factor particularly for nonlinear systems, whereby this number depends exponentially on the nonlinear system order. We illustrate the performance of the proposed scheme by presenting results for a simulated linear two-input system with time-varying characteristics.
authors.affiliation: Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus. gmitsis@ucy.ac.cy
authors.forename: Georgios D
authors.initials: GD
authors.lastname: Mitsis
authors.forename: Marios M
authors.initials: MM
authors.lastname: Markou
mesh_terms: D016018:Least-Squares Analysis; D017711:Nonlinear Dynamics