Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Adaptation of a memoryless preprocessor for nonlinear acoustic echo cancelling
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Brief paper: Iterative identification of Hammerstein systems
Automatica (Journal of IFAC)
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Kernel Adaptive Filtering: A Comprehensive Introduction
Kernel Adaptive Filtering: A Comprehensive Introduction
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
A stochastic gradient adaptive filter with gradient adaptive stepsize
IEEE Transactions on Signal Processing
A blind approach to Hammerstein model identification
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
A fast quasi-Newton adaptive filtering algorithm
IEEE Transactions on Signal Processing
A general class of nonlinear normalized adaptive filteringalgorithms
IEEE Transactions on Signal Processing
Stochastic mean-square performance analysis of an adaptive Hammerstein filter
IEEE Transactions on Signal Processing - Part I
A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
Multilayer feedforward networks with adaptive spline activation function
IEEE Transactions on Neural Networks
Nonlinear spline adaptive filtering
Signal Processing
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In this paper a novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented. The nonlinear function involved in the adaptation process is based on a uniform cubic spline function that can be properly modified during learning. The spline control points are adaptively changed by using gradient-based techniques. This new kind of adaptive function is then applied to the input of a linear adaptive filter and it is used for the identification of Hammerstein-type nonlinear systems. In addition, we derive a simple form of the adaptation algorithm, an upper bound on the choice of the step-size and a lower bound on the excess mean square error in a theoretical manner. Some experimental results are also presented to demonstrate the effectiveness of the proposed method in the identification of high-order nonlinear systems.