A bibliography on nonlinear system identification
Signal Processing - Special section on digital signal processing for multimedia communications and services
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Penalized least squares estimation of Volterra filters and higherorder statistics
IEEE Transactions on Signal Processing
Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise
Digital Signal Processing
Hi-index | 0.00 |
Volterra and Wiener series are perhaps the best-understood nonlinear system representations in signal processing. Although both approaches have enjoyed a certain popularity in the past, their application has been limited to rather low-dimensional and weakly nonlinear systems due to the exponential growth of the number of terms that have to be estimated. We show that Volterra and Wiener series can be represented implicitly as elements of a reproducing kernel Hilbert space by using polynomial kernels. The estimation complexity of the implicit representation is linear in the input dimensionality and independent of the degree of nonlinearity. Experiments show performance advantages in terms of convergence, interpretability, and system sizes that can be handled.