Structural classification of multi-input nonlinear systems.
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.
Chen
H W
HW
Department of Neurology, University of Massachusetts Medical School, Worcester 01655.
Jacobson
L D
LD
Gaska
J P
JP
D003491:Cybernetics; D016014:Linear Models