System identification: theory for the user
System identification: theory for the user
Nonlinear Control Systems
Distortion Analysis of Analog Integrated Circuits
Distortion Analysis of Analog Integrated Circuits
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Projection-based approaches for model reduction of weakly nonlinear, time-varying systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The special form of the Laplace-domain Volterra kernels for linear-analytic systems is exploited to obtain an approximate model that obeys an appealingly simple feedforward block structure. It comprises a composition of the linearization and the multivariate nonlinear function of the original system. The model does not involve a truncation in the power series expansion nor in the memory depths and offers an economic parameterization. It is shown to be linearly identifiable in one step if a priori information about the linearized dynamics is provided. We present simulation results for a simple nonlinear circuit showing the validity of the model.