title: Structural classification of multi-input nonlinear systems.
abstract: We present new structural classification and parameter estimation results that are applicable to multi-input nonlinear systems. The mathematical relationships between the self- and cross-(Volterra and Wiener) kernels are derived for a basic two-input nonlinear structure. These results are then used to develop classification methods for more complicated two-input structures. Algorithms for estimating the parameters (linear and nonlinear subsystems) of these structures are also presented.
authors.affiliation: Department of Neurology, University of Massachusetts Medical School, Worcester 01655.
authors.forename: H W
authors.initials: HW
authors.lastname: Chen
authors.forename: L D
authors.initials: LD
authors.lastname: Jacobson
authors.forename: J P
authors.initials: JP
authors.lastname: Gaska
mesh_terms: D003491:Cybernetics; D016014:Linear Models