System identification
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Ten lectures on wavelets
Neural networks for pattern recognition
Neural networks for pattern recognition
Nonlinear feedback control systems: an operator theory approach
Nonlinear feedback control systems: an operator theory approach
A friendly guide to wavelets
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Wavelets and time-frequency methods in linear systems and neural networks
Wavelets and time-frequency methods in linear systems and neural networks
Approximate models for nonlinear dynamical systems and their generalization properties
Mathematical and Computer Modelling: An International Journal
Rational wavelets in Wiener-like modeling
Mathematical and Computer Modelling: An International Journal
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In this paper we present three examples that show the applications of a black-box identification structure already defined. This structure can be described as a concatenation of a mapping from observed data to a finite set of linear filters realized using rational wavelets, and a nonlinear mapping from the output of the linear dynamic part to the system output represented by a hidden layer perceptron neural network, or a basis (that might be orthonormal) of high level canonical piecewise linear functions. The wavelets used for identifying the linear dynamic part are selected taking into account the linear dynamics of the system and consequently they can be considered as semiphysical regressors. Also, this structure allows to approximate the dynamic evolution of any nonlinear, causal, time-invariant system with fading memory.