The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Statistical Learning Theory: A Primer
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
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
WSEAS Transactions on Circuits and Systems
Induction machine fault detection using support vector machine based classifier
WSEAS TRANSACTIONS on SYSTEMS
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In this paper, we consider the modelling of a Single Input Single Output (SISO) and Multi Input Multi Output (MIMO) non linear communication channels using two modelling techniques. The first titled Volterra model built using Volterra series and possesses several important properties that make them very useful for the modelling and analysis of non linear systems and the second named RKHS model 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 RKHS model. The performances of both models in SISO case and in MIMO case are evaluated and the results were successful.