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Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett
  • Author : Anatoli Torokhti
  • Publsiher : Elsevier
  • Release : 11 April 2007
  • ISBN : 9780080475387
  • Pages : 322 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKComputational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

Summary:
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. Best operator approximation Non-Lagrange interpolation Generic Karhunen-Loeve transform Generalised low-rank matrix approximation Optimal data compression Optimal nonlinear filtering


Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett
  • Author : Anatoli Torokhti,Phil Howlett
  • Publisher : Elsevier
  • Release : 11 April 2007
GET THIS BOOKComputational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods

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Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
  • Author : Danilo Comminiello,Jose C. Principe
  • Publisher : Butterworth-Heinemann
  • Release : 11 June 2018
GET THIS BOOKAdaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity

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Computational Methods for Modeling of Nonlinear Systems

Computational Methods for Modeling of Nonlinear Systems
  • Author : Anatoli Torokhti
  • Publisher : Anonim
  • Release : 20 August 1981
GET THIS BOOKComputational Methods for Modeling of Nonlinear Systems

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods

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Introduction to Stochastic Control Theory

Introduction to Stochastic Control Theory
  • Author : Anonim
  • Publisher : Elsevier Science
  • Release : 12 December 1970
GET THIS BOOKIntroduction to Stochastic Control Theory

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods

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Computational Methods for Modeling of Nonlinear Systems

Computational Methods for Modeling of Nonlinear Systems
  • Author : Anatoli Torokhti,Phil Howlett
  • Publisher : Elsevier Science Limited
  • Release : 20 August 1967
GET THIS BOOKComputational Methods for Modeling of Nonlinear Systems

In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods

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Concrete Operators, Spectral Theory, Operators in Harmonic Analysis and Approximation

Concrete Operators, Spectral Theory, Operators in Harmonic Analysis and Approximation
  • Author : Manuel Cepedello Boiso,Håkan Hedenmalm,Marinus A. Kaashoek,Alfonso Montes Rodríguez,Sergei Treil
  • Publisher : Springer Science & Business Media
  • Release : 04 November 2013
GET THIS BOOKConcrete Operators, Spectral Theory, Operators in Harmonic Analysis and Approximation

This book contains a collection of research articles and surveys on recent developments on operator theory as well as its applications covered in the IWOTA 2011 conference held at Sevilla University in the summer of 2011. The topics include spectral theory, differential operators, integral operators, composition operators, Toeplitz operators, and more. The book also presents a large number of techniques in operator theory.

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Numerical Methods for Nonlinear Partial Differential Equations

Numerical Methods for Nonlinear Partial Differential Equations
  • Author : Sören Bartels
  • Publisher : Springer
  • Release : 19 January 2015
GET THIS BOOKNumerical Methods for Nonlinear Partial Differential Equations

The description of many interesting phenomena in science and engineering leads to infinite-dimensional minimization or evolution problems that define nonlinear partial differential equations. While the development and analysis of numerical methods for linear partial differential equations is nearly complete, only few results are available in the case of nonlinear equations. This monograph devises numerical methods for nonlinear model problems arising in the mathematical description of phase transitions, large bending problems, image processing, and inelastic material behavior. For each of these

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Computational Modelling of Concrete Structures

Computational Modelling of Concrete Structures
  • Author : Nenad Bicanic,René Borst,Herbert Mang,Gunther Meschke
  • Publisher : CRC Press
  • Release : 24 February 2010
GET THIS BOOKComputational Modelling of Concrete Structures

Since 1984 the EURO-C conference series (Split 1984, Zell am See 1990, Innsbruck 1994, Badgastein 1998, St Johann im Pongau 2003, Mayrhofen 2006, Schladming 2010) has provided a forum for academic discussion of the latest theoretical, algorithmic and modelling developments associated with computational simulations of concrete and concrete structure

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Numerical Methods for Nonlinear Engineering Models

Numerical Methods for Nonlinear Engineering Models
  • Author : John R. Hauser
  • Publisher : Springer Science & Business Media
  • Release : 24 March 2009
GET THIS BOOKNumerical Methods for Nonlinear Engineering Models

There are many books on the use of numerical methods for solving engineering problems and for modeling of engineering artifacts. In addition there are many styles of such presentations ranging from books with a major emphasis on theory to books with an emphasis on applications. The purpose of this book is hopefully to present a somewhat different approach to the use of numerical methods for - gineering applications. Engineering models are in general nonlinear models where the response of some

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Applications of Turbulent and Multi-Phase Combustion

Applications of Turbulent and Multi-Phase Combustion
  • Author : Kenneth Kuan-yun Kuo,Ragini Acharya
  • Publisher : John Wiley & Sons
  • Release : 01 May 2012
GET THIS BOOKApplications of Turbulent and Multi-Phase Combustion

"This book is the second of two follow-on volumes to the author's bestseller, Principles of Combustion, Second Edition published in 2005. This text focuses on applications, with coverage not available elsewhere, including solid propellants, burning behavior, and chemical boundary layer flows. Kuo provides a multiphase systems approach beginning with more common topics and moving to higher level applications. As with Kuo's earlier book, large numbers of examples and problems and a solutions manual are provided"--Provided by publisher.

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Computational Solution of Nonlinear Systems of Equations

Computational Solution of Nonlinear Systems of Equations
  • Author : Eugene L. Allgower,Kurt Georg
  • Publisher : American Mathematical Soc.
  • Release : 03 April 1990
GET THIS BOOKComputational Solution of Nonlinear Systems of Equations

Nonlinear equations arise in essentially every branch of modern science, engineering, and mathematics. However, in only a very few special cases is it possible to obtain useful solutions to nonlinear equations via analytical calculations. As a result, many scientists resort to computational methods. This book contains the proceedings of the Joint AMS-SIAM Summer Seminar, ``Computational Solution of Nonlinear Systems of Equations,'' held in July 1988 at Colorado State University. The aim of the book is to give a wide-ranging survey

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Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control

Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control
  • Author : Christian Kirches
  • Publisher : Springer Science & Business Media
  • Release : 23 November 2011
GET THIS BOOKFast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control

Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding.

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Model Reduction of Nonlinear Mechanical Systems Via Optimal Projection and Tensor Approximation

Model Reduction of Nonlinear Mechanical Systems Via Optimal Projection and Tensor Approximation
  • Author : Anonim
  • Publisher : Stanford University
  • Release : 20 August 2022
GET THIS BOOKModel Reduction of Nonlinear Mechanical Systems Via Optimal Projection and Tensor Approximation

Despite the advent and maturation of high-performance computing, high-fidelity physics-based numerical simulations remain computationally intensive in many fields. As a result, such simulations are often impractical for time-critical applications such as fast-turnaround design, control, and uncertainty quantification. The objective of this thesis is to enable rapid, accurate analysis of high-fidelity nonlinear models to enable their use in time-critical settings. Model reduction presents a promising approach for realizing this goal. This class of methods generates low-dimensional models that preserves key features

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