Results 281 to 290 of about 1,828,697 (335)

Nonlinear Programming for System Identification

IFAC Proceedings Volumes, 1979
Abstract Numerical procedures for dynamic system identification are discussed. Efficient algorithms for static least-squares problems provide a starting point for dynamic systems nonlinear programming methods. This paper shows that in dynamic systems, the additional computation time required for the first and the second gradients over function ...
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Identification of Nonlinear Systems

1989 American Control Conference, 1989
It is extremely difficult to identify general nonlinear systems because of the number of unknowns involved. Moreover, since most design techniques assume a linear model, many of the nonlinearities that can be determined are essentially ignored. Why not develop nonlinear system identification techniques to reveal restricted classes of nonlinear systems ...
L.R. Hunt, R.D. DeGroat, D.A. Linebarger
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Nonlinear System Identification

2010
This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended ...
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Multiscale nonlinear system identification

2007 46th IEEE Conference on Decision and Control, 2007
Multiscale wavelet-based representation is a powerful data analysis and feature extraction tool. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of nonlinear models by developing a multiscale nonlinear (MSNL) system identification algorithm. In particular, we consider the class of linear-in-
Mohamed N. Nounou, Hazem N. Nounou
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Koopman Operator Inspired Nonlinear System Identification

SIAM Journal on Applied Dynamical Systems, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the Identification of Nonlinear Systems

IFAC Proceedings Volumes, 1982
Abstract For the identification of systems in which the nonlinear element is in the feedback path, a new technique based on the Volterra characterisation of nonlinear system, is presented. The method is shown to have distinct computational advantages. Simulation studies using the proposed method are given.
N.C. Jagan, D.C. Reddy
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Nonlinear System Identification: An Overview of Common Approaches

2014
Nonlinear mathematical models are essential tools in various engineering and scientific domains, where more and more data are recorded by electronic devices. How to build nonlinear mathematical models essentially based on experimental data is the topic of this entry.
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A bibliography on nonlinear system identification

Signal Processing, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Giannakis, G. B., Serpedin, E.
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Efficient nonlinear system identification

ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
System identification of a second order truncated Volterra series with correlated and Gaussian input is investigated. This problem has been treated by Schetzen using Wiener nonlinear theory. In this paper we show how this nonlinear system can be efficient|y identified using Gaussian properties of the input.
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Identification of Nonlinear Systems

IFAC Proceedings Volumes, 1994
Abstract A method is proposed for approximating dynamic nonlinear systems using parallel cascades of alternating dynamic linear and static nonlinear elements. A key advantage of the proposed method is its effectiveness in approximating nonlinear systems which cannot be well fit using the first few terms of a Volterra series.
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