The nature of statistical learning theory
The nature of statistical learning theory
Reduced complexity Volterra models for nonlinear system identification
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
The Volterra and Wiener Theories of Nonlinear Systems
The Volterra and Wiener Theories of Nonlinear Systems
MIMO Volterra filter equalization using pth-order inverse approach
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
| Hi-index | 0.00 |
During the last decade, Volterra Model has been considered as the most usual and popular way to handle non linear systems. As an alternative to this modelling strategy the last few years has registered the birth of a new modelling technique developed on a particular Hilbert Space the kernel of which is reproducing. This space known as Reproducing Kernel Hilbert Space (RKHS) uses the statistical learning theory to provide an RKHS model. This paper proposes the modelling of a non linear Multi Input Multi Output (MIMO) communication channel with two modelling techniques reduced MIMO Volterra model and RKHS MIMO. The performances of both models are evaluated and the results were successful.