Adaptive GPC based on Laguerre-filters modelling
Automatica (Journal of IFAC)
On approximation of stable linear dynamical systems using Laguerre and Kautz functions
Automatica (Journal of IFAC)
Discrete Orthonormal Sequences
Journal of the ACM (JACM)
Online optimization of the time scale in adaptive Laguerre-basedfilters
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
RF power amplifier modeling using polynomials with IIR bases functions
RWS'09 Proceedings of the 4th international conference on Radio and wireless symposium
RF power amplifier modeling for three-port applications using polynomials with IIR bases functions
RWS'10 Proceedings of the 2010 IEEE conference on Radio and wireless symposium
Technical communique: A note on the optimal expansion of Volterra models using Laguerre functions
Automatica (Journal of IFAC)
Differential evolution-based nonlinear system modeling using a bilinear series model
Applied Soft Computing
Hi-index | 22.15 |
This work is concerned with the optimization of Laguerre bases for the orthonormal series expansion of discrete-time Volterra models. The aim is to minimize the number of Laguerre functions associated with a given series truncation error, thus reducing the complexity of the resulting finite-dimensional representation. Fu and Dumont (IEEE Trans. Automatic Control 38(6) (1993) 934) indirectly approached this problem in the context of linear systems by minimizing an upper bound for the error resulting from the truncated Laguerre expansion of impulse response models, which are equivalent to first-order Volterra models. A generalization of the work mentioned above focusing on Volterra models of any order is presented in this paper. The main result is the derivation of analytic strict global solutions for the optimal expansion of the Volterra kernels either using an independent Laguerre basis for each kernel or using a common basis for all the kernels.