Lagrange programming neural networks for blind Volterra system modelling
Tania Stathaki, Anthony George Constantinides · 2002
In this paper the problem of nonlinear signal modelling is examined from a mixed order statistical perspective. The approach taken involves the use of second order Volterra kernels which are derived from a joint operation on second and third order moments of the signals. The paper describes the fundamental issues of the various components of the approach both for one dimensional and two dimensional signals. The nonlinear equations involved are solved by means of unconstrained Lagrange programming neural networks. The Volterra kernels may be used in further operations such as features for image classification and segmentation.