Adaptive filter theory
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A bibliography on nonlinear system identification
Signal Processing - Special section on digital signal processing for multimedia communications and services
Kernel independent component analysis
The Journal of Machine Learning Research
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Kernel least-squares models using updates of the pseudoinverse
Neural Computation
EURASIP Journal on Applied Signal Processing
On Nonparametric Identification of Wiener Systems
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Using articulatory likelihoods in the recognition of dysarthric speech
Speech Communication
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This paper treats the identification of nonlinear systems that consist of a cascade of a linear channel and a nonlinearity, such as the well-known Wiener and Hammerstein systems. In particular, we follow a supervised identification approach that simultaneously identifies both parts of the nonlinear system. Given the correct restrictions on the identification problem, we show how kernel canonical correlation analysis (KCCA) emerges as the logical solution to this problem. We then extend the proposed identification algorithm to an adaptive version allowing to deal with time-varying systems. In order to avoid overfitting problems, we discuss and compare three possible regularization techniques for both the batch and the adaptive versions of the proposed algorithm. Simulations are included to demonstrate the effectiveness of the presented algorithm.