Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Identification of Nonlinear Physiological Systems
Identification of Nonlinear Physiological Systems
Adaptive Nonlinear System Indentification: The Volterra and Wiener Model Approaches
Adaptive Nonlinear System Indentification: The Volterra and Wiener Model Approaches
Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling
SIAM Journal on Control and Optimization
Nonlinearity estimation in Hammerstein systems based on orderedobservations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind identification of linear subsystems of LTI-ZMNL-LTI modelswith cyclostationary inputs
IEEE Transactions on Signal Processing
A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals
IEEE Transactions on Signal Processing
Brief Identification of linear systems with hard input nonlinearities of known structure
Automatica (Journal of IFAC)
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Brief Parameter identification of a class of Hammerstein plants
Automatica (Journal of IFAC)
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The primary difficulty in the identification of Hammerstein nonlinear systems (a static memoryless nonlinear system in series with a dynamic linear system) is that the output of the nonlinear system (input to the linear system) is unknown. By employing the theory of affine projection, we propose a gradient-based adaptive Hammerstein algorithm with variable step-size which estimates the Hammerstein nonlinear system parameters. The adaptive Hammerstein nonlinear system parameter estimation algorithm proposed is accomplished without linearizing the systems nonlinearity. To reduce the effects of eigenvalue spread as a result of the Hammerstein system nonlinearity, a new criterion that provides a measure of how close the Hammerstein filter is to optimum performance was used to update the step-size. Experimental results are presented to validate our proposed variable step-size adaptive Hammerstein algorithm given a real life system and a hypothetical case.