Journal ArticleDOI
On the Volterra series functional evaluation of the response of non-linear discrete-time systems
F. C. Fu,James B. Farison +1 more
TLDR
In this article, the Volterra kernels that characterize the various terms representing the free and forced response components are defined, and a convolution-type relationship between these kernels is developed.Abstract:
The discrete form of the Volterra series is used to evaluate the response of non-linear discrete-time systems. The Volterra kernels that characterize the various terms representing the free and forced response components are defined, and a convolution-type relationship between these kernels is developed.read more
Citations
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Journal ArticleDOI
A bibliography on nonlinear system identification
TL;DR: The present bibliography represents a comprehensive list of references on nonlinear system identification and its applications in signal processing, communications, and biomedical engineering.
Journal ArticleDOI
Cluster-based network modeling—From snapshots to complex dynamical systems
TL;DR: Cluster-based network modeling (CNM) as mentioned in this paper is a universal data-driven representation of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge, which can describe short and long-term behavior and is fully automatable.
Journal ArticleDOI
Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section
TL;DR: In this paper, a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section was developed and tested for active vibration suppression of the model subjected to parametric excitation.
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Cluster-based network modeling -- automated robust modeling of complex dynamical systems
TL;DR: Cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics is proposed, which only assumes smoothness of the dynamics in the state space, robustly describes short- and long-term behavior and is fully automatable as it does not rely on application-specific knowledge.
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Bayesian Dynamical System Identification With Unified Sparsity Priors And Model Uncertainty.
TL;DR: In this paper, the authors formulate the sparse-identification of nonlinear dynamics (SINDy) framework as a regression problem, where unknown functions are approximated from a sparse subset of an underlying library.
References
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Journal ArticleDOI
A consideration of the discrete Volterra series
TL;DR: In this paper, the authors used multidimensional Z transforms and the discrete form of the Volterra series to analyze a large class of nonlinear sampled-data systems and nonlinear difference equations, presenting the solution in terms of the kernels of the VOLTERRA series.
Journal ArticleDOI
Analysis of a class of non-linear discrete-time systems by the Volterra series†
F. C. Fu,James B. Farison +1 more
TL;DR: The discrete form of the Volterra series is used to evaluate the response of a class of non-linear discrete-time systems described by an ordinary nonlinear difference equation with zero initial conditions.