Synthesis of minimal cost nonlinear feedback shift registers
Signal Processing
A factorization method for identification of Volterra systems
Journal of Computational and Applied Mathematics - Selected papers of the international symposium on applied mathematics, August 2000, Dalian, China
Blind identification of second order Hammerstein series
Signal Processing
Advances in Lee--Schetzen Method for Volterra Filter Identification
Multidimensional Systems and Signal Processing
Adaptive nonlinear system identification in the short-time fourier transform domain
IEEE Transactions on Signal Processing
Modeling and identification of nonlinear systems in the short-time fourier transform domain
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Adaptive algorithms for sparse system identification
Signal Processing
Identification of discrete-time state affine state space models using cumulants
Automatica (Journal of IFAC)
| Hi-index | 35.70 |
In this paper, nonlinear filtering and identification based on finite-support Volterra models are considered. The Volterra kernels are estimated via input-output statistics or directly in terms of input-output data. It is shown that the normal equations for a finite-support Volterra system excited by zero mean Gaussian input have a unique solution if, and only if, the power spectral process of the input signal is nonzero at least at m distinct frequencies, where m is the memory of the system. A multichannel embedding approach is introduced. A set of primary signals defined in terms of the input signal serve to map efficiently the nonlinear process to an equivalent multichannel format. Efficient algorithms for the estimation of the Volterra parameters are derived for batch, as well as for adaptive processing. An efficient order-recursive method is presented for the determination of the Volterra model structure. The proposed methods are illustrated by simulations