Alberto Isidori · Medical Entomology and Zoology · 1999
Chapter headings: Controller Design for Nonlinear Systems. Sliding Mode Control of Nonlinear Systems. H-Infinity and Nonlinear Control. Optimization and Related Topics I. Optimization and Related Topics II. Advanced Applications of Genetic Algorithms. Linear Quadratic Optimal Control. Control Applications of Optimization. Optimal Control I. Optimal Control II. H-Infinity Control and Game Theoretical Approach. Optimal Control Design. Nonlinear Robust Control I. Nonlinear Robust Control II. Optimal, Robust Control I. Optimal, Robust Control II. Author index. Keyword index.
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Oliver Nelles · 2020
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Oliver Nelles · Measurement Science and Technology · 2002
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Yonathan Bard, Leon Lapidus · Industrial & Engineering Chemistry Fundamentals · 1970
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTNonlinear System IdentificationYonathan Bard and Leon LapidusCite this: Ind. Eng. Chem. Fundamen. 1970, 9, 4, 628–633Publication Date (Print):November 1, 1970Publication History Published online1 May 2002Published inissue 1 November 1970https://pubs.acs.org/doi/10.1021/i160036a018https://doi.org/10.1021/i160036a018research-articleACS PublicationsRequest reuse permissionsArticle Views121Altmetric-Citations7LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access options Get e-Alerts
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Casti, J. L. · 1985
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Mathematics in Science and Engineering/Mathematics in science and engineering · 1985
Cited by 499
Azad Siahmakoun, Thomas Harrer · 1993
In this paper we will present a dynamic phase filter system using optical phase conjugation in a photorefractive crystal. The theoretical description of the system reveals that the square of phase changes in an objec field can be mapped as the intensity modulation in the system output. This has been demonstrated experimentally using a single beam from HeCd laser and a BaTiO 3 crystal arranged in the self-pumped phase-conjugator (SPPC) configuration.
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Edward H. Ziegler · RIT Scholar Works (Rochester Institute of Technology) · 1994
The prediction of a single observable time series has been achieved with reasonable accuracy and duration for the nonlinear systems developed by Rossler and Lorenz. Based on Takens' Delay-vector Space, an artificial system has been generated using a polynomial least squares technique that includes all possible fifth order combinations of the vectors in the delay space. Furthermore, an optimum shift value has been shown to exist, such that any deviation decreases the accuracy and stability of the prediction. Additionally, an augmented form of the autocorrelation function, similar to the delay vector expansion, has been investigated. The first inflection of this correlation, typically in the dimension of the system, tends to coincide with the optimum shift value required for the best prediction. This method has also been utilized in conjunction with the Grassberger-Procaccia Distance correlation function to accurately determine the fractal dimension of the systems being investigated.
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Bin Hasnan, Hairul Hafizi · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2020
This thesis presents a novel massively mutual compression system for use as a nonlinear mixing tool. In this interdisciplinary work, the system is explained and explored from the perspectives of both audio engineering and music production aesthetics. First, the traditional method for presenting the time-frequency energy distribution of musical audio signals, the short-time Fourier transform, is assessed. Following that, an alternative, the constant-Q transform, is suggested as a better approach to illustrating variations in time-frequency content of audio signals. By exploring the architecture of digital compressors, a method to create a bilateral cross-adaptive architecture is proposed and created in the Max/MSP visual programming language. This platform is chosen because of its routing flexibility and performance. In this thesis, it was found that commercially available digital audio workstation applications are not able to support the feedback cross-modulation which is crucial for this architecture. The behaviour of the system, in particular a ‘double knee’ curve, is characterised, verified, and explained by a series of tests. Then, two original audio examples are presented which are mixed in different ways to highlight how the system developed in this research can be utilised. These examples highlight some unique properties of this system. The first is the lack of one perpetually dominant element in the mix. Secondly, constant changes are introduced into the tonal balance of the mix. The research also found that the ‘top down’ approach to mixing is the most intuitive approach to using the system. Finally, user tests were conducted involving 15 participants. The experiment found that users will most likely use the architecture as a mixing tool to reduce masking. Furthermore, most of the participants agree that there are advantages of utilising this tool in ther production. Additionally, a majority of the respondents have discovered mixing technique that are unique to the nonlinear mixing system.
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Leding Ren · Zenodo (CERN European Organization for Nuclear Research) · 2026
First public draft (v0.1) of Nonlinear System Overview.This release represents an initial formulation of the framework. This work presents a structural framework for nonlinear systems, focusing on dissipation, irreversibility, and structure formation. Rather than treating randomness and irreversibility as limitations, this work interprets them as structural outcomes of constrained systems. This draft focuses on conceptual structure rather than empirical validation or application. Author: Leding RenAffiliation: Independent Researcher This release is archived for citation and DOI generation via Zenodo.
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Brian F. Feeny · Elsevier eBooks · 2001
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S.A. Billings · 2013
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Moses Hadas · Chapman & Hall/CRC applied mathematics and nonlinear science series · 2008
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Zhengbin Li (14259417), Lijun Ma (207865), Yongqiang Wang (211682) · Figshare · 2022
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Wiener Approach · 2002
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Leding Ren · Zenodo (CERN European Organization for Nuclear Research) · 2026
First public draft (v0.1) of Nonlinear System Overview.This release represents an initial formulation of the framework. This work presents a structural framework for nonlinear systems, focusing on dissipation, irreversibility, and structure formation. Rather than treating randomness and irreversibility as limitations, this work interprets them as structural outcomes of constrained systems. This draft focuses on conceptual structure rather than empirical validation or application. Author: Leding RenAffiliation: Independent Researcher This release is archived for citation and DOI generation via Zenodo.
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Jagiellonian University Repository (Jagiellonian University) · 2008
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Arun N. Netravali · Rice Digital Scholarship Archive (Rice University) · 1969
Stochastic approximation methods for the identification of parameters of nonlinear systems without dynamics have been widely discussed in the literature. In this work, two classes of discrete-time nonlinear dynamical systems driven by independent noise are considered. The measurements are assumed to be linear scalar and are made in the presence of independent noise. The systems under consideration are identified by the estimation of the parameters appearing in the evolution operator. These parameters are assumed to be constant during the identification time and they are estimated by means of stochastic approximation algorithms. A computer algorithm based on the above method is used to identify the parameters for some typical examples.
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Wiley series in probability and statistics · 2010
This chapter reviews some recommended techniques to identify the frequency domain properties of nonlinear systems from measured input/output random data. Procedures are discussed for the following five types of nonlinear systems and models: (1) zero-memory and finite-memory nonlinear systems; (2) square-law and cubic nonlinear models; (3) volterra nonlinear models; (4) single-input/single-output (SI/SO) models with parallel linear and nonlinear systems; (5) SI/SO models with nonlinear feedback. Two main properties distinguish nonlinear systems from linear systems. First, nonlinear systems do not satisfy the additive and homogeneous requirements of linear systems. Second, the passage of Gaussian input data through any nonlinear system produces non-Gaussian output data. Two engineering applications of SI/SO nonlinear models are discussed: duffing SDOF nonlinear system and nonlinear drift force problem. Controlled Vocabulary Terms Gaussian Process; input/output process; nonlinear model
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Alan G. Haddow · Elsevier eBooks · 2001
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