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
Publsiher: Elsevier
Total Pages: 322
Release: 2007-04-11
ISBN 10: 9780080475387
ISBN 13: 0080475388
Language: EN, FR, DE, ES & NL

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

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

Computational Methods for Modeling of Nonlinear Systems
Author: Anatoli Torokhti,Phil Howlett
Publsiher: Elsevier Science Limited
Total Pages: 322
Release: 1968
ISBN 10: 9780126789508
ISBN 13: 0126789509
Language: EN, FR, DE, ES & NL

Computational Methods for Modeling of Nonlinear Systems Book Review:

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

Introduction to Stochastic Control Theory

Introduction to Stochastic Control Theory
Author: Anonim
Publsiher: Elsevier Science
Total Pages: 322
Release: 1970-12-12
ISBN 10: 9780120656509
ISBN 13: 0120656507
Language: EN, FR, DE, ES & NL

Introduction to Stochastic Control Theory Book Review:

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

Dynamic Programming and Its Application to Optimal Control

Dynamic Programming and Its Application to Optimal Control
Author: Anonim
Publsiher: Elsevier Science
Total Pages: 322
Release: 1971-10-11
ISBN 10: 9780080955896
ISBN 13: 0080955894
Language: EN, FR, DE, ES & NL

Dynamic Programming and Its Application to Optimal Control Book Review:

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

Nonlinear Systems Vol 1

Nonlinear Systems  Vol  1
Author: Victoriano Carmona,Jesús Cuevas-Maraver,Fernando Fernández-Sánchez,Elisabeth García- Medina
Publsiher: Springer
Total Pages: 424
Release: 2018-09-15
ISBN 10: 3319667661
ISBN 13: 9783319667669
Language: EN, FR, DE, ES & NL

Nonlinear Systems Vol 1 Book Review:

This book is part of a two volume set which presents the analysis of nonlinear phenomena as a long-standing challenge for research in basic and applied science as well as engineering. It discusses nonlinear differential and differential equations, bifurcation theory for periodic orbits and global connections. The integrability and reversibility of planar vector fields and theoretical analysis of classic physical models are sketched. This first volume concentrates on the mathematical theory and computational techniques that are essential for the study of nonlinear science, a second volume deals with real-world nonlinear phenomena in condensed matter, biology and optics.

Adaptive Learning Methods for Nonlinear System Modeling

Adaptive Learning Methods for Nonlinear System Modeling
Author: Danilo Comminiello,Jose C. Principe
Publsiher: Butterworth-Heinemann
Total Pages: 388
Release: 2018-06-11
ISBN 10: 0128129778
ISBN 13: 9780128129777
Language: EN, FR, DE, ES & NL

Adaptive Learning Methods for Nonlinear System Modeling Book Review:

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 introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.

Advanced Computational Methods in Life System Modeling and Simulation

Advanced Computational Methods in Life System Modeling and Simulation
Author: Minrui Fei,Shiwei Ma,Xin Li,Xin Sun,Li Jia,Zhou Su
Publsiher: Springer
Total Pages: 609
Release: 2017-09-01
ISBN 10: 9811063702
ISBN 13: 9789811063701
Language: EN, FR, DE, ES & NL

Advanced Computational Methods in Life System Modeling and Simulation Book Review:

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.

Computational Techniques for Modelling Learning in Economics

Computational Techniques for Modelling Learning in Economics
Author: Thomas Brenner
Publsiher: Springer Science & Business Media
Total Pages: 391
Release: 2012-12-06
ISBN 10: 1461550297
ISBN 13: 9781461550297
Language: EN, FR, DE, ES & NL

Computational Techniques for Modelling Learning in Economics Book Review:

Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.

Nonlinear Analysis Problems Applications and Computational Methods

Nonlinear Analysis  Problems  Applications and Computational Methods
Author: Zakia Hammouch,Hemen Dutta,Said Melliani,Michael Ruzhansky
Publsiher: Springer Nature
Total Pages: 249
Release: 2020-11-13
ISBN 10: 3030622991
ISBN 13: 9783030622992
Language: EN, FR, DE, ES & NL

Nonlinear Analysis Problems Applications and Computational Methods Book Review:

This book is a collection of original research papers as proceedings of the 6th International Congress of the Moroccan Society of Applied Mathematics organized by Sultan Moulay Slimane University, Morocco, during 7th–9th November 2019. It focuses on new problems, applications and computational methods in the field of nonlinear analysis. It includes various topics including fractional differential systems of various types, time-fractional systems, nonlinear Jerk equations, reproducing kernel Hilbert space method, thrombin receptor activation mechanism model, labour force evolution model, nonsmooth vector optimization problems, anisotropic elliptic nonlinear problem, viscous primitive equations of geophysics, quadratic optimal control problem, multi-orthogonal projections and generalized continued fractions. The conference aimed at fostering cooperation among students, researchers and experts from diverse areas of applied mathematics and related sciences through fruitful deliberations on new research findings. This book is expected to be resourceful for researchers, educators and graduate students interested in applied mathematics and interactions of mathematics with other branches of science and engineering.

Partial Differential Equations

Partial Differential Equations
Author: Roland Glowinski,Pekka Neittaanmäki
Publsiher: Springer Science & Business Media
Total Pages: 292
Release: 2008-06-26
ISBN 10: 1402087586
ISBN 13: 9781402087585
Language: EN, FR, DE, ES & NL

Partial Differential Equations Book Review:

For more than 250 years partial di?erential equations have been clearly the most important tool available to mankind in order to understand a large variety of phenomena, natural at ?rst and then those originating from - man activity and technological development. Mechanics, physics and their engineering applications were the ?rst to bene?t from the impact of partial di?erential equations on modeling and design, but a little less than a century ago the Schr ̈ odinger equation was the key opening the door to the application of partial di?erential equations to quantum chemistry, for small atomic and molecular systems at ?rst, but then for systems of fast growing complexity. The place of partial di?erential equations in mathematics is a very particular one: initially, the partial di?erential equations modeling natural phenomena were derived by combining calculus with physical reasoning in order to - press conservation laws and principles in partial di?erential equation form, leading to the wave equation, the heat equation, the equations of elasticity, the Euler and Navier–Stokes equations for ?uids, the Maxwell equations of electro-magnetics, etc. It is in order to solve ‘constructively’ the heat equation that Fourier developed the series bearing his name in the early 19th century; Fourier series (and later integrals) have played (and still play) a fundamental roleinbothpureandappliedmathematics,includingmanyareasquiteremote from partial di?erential equations. On the other hand, several areas of mathematics such as di?erential ge- etry have bene?ted from their interactions with partial di?erential equations.

Numerical Methods for Nonlinear Partial Differential Equations

Numerical Methods for Nonlinear Partial Differential Equations
Author: Sören Bartels
Publsiher: Springer
Total Pages: 393
Release: 2015-01-19
ISBN 10: 3319137972
ISBN 13: 9783319137971
Language: EN, FR, DE, ES & NL

Numerical Methods for Nonlinear Partial Differential Equations Book Review:

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 problems the underlying mathematical model is discussed, the essential analytical properties are explained, and the proposed numerical method is rigorously analyzed. The practicality of the algorithms is illustrated by means of short implementations.

Computational Methods for Parameter Estimation in Nonlinear Models

Computational Methods for Parameter Estimation in Nonlinear Models
Author: Bryan Andrew Toth
Publsiher: Unknown
Total Pages: 167
Release: 2011
ISBN 10: 9781124694764
ISBN 13: 1124694765
Language: EN, FR, DE, ES & NL

Computational Methods for Parameter Estimation in Nonlinear Models Book Review:

This dissertation expands on existing work to develop a dynamical state and parameter estimation methodology in non-linear systems. The field of parameter and state estimation, also known as inverse problem theory, is a mature discipline concerned with determining unmeasured states and parameters in experimental systems. This is important since measurement of some of the parameters and states may not be possible, yet knowledge of these unmeasured quantities is necessary for predictions of the future state of the system. This field has importance across a broad range of scientific disciplines, including geosciences, biosciences, nanoscience, and many others. he work presented here describes a state and parameter estimation method that relies on the idea of synchronization of nonlinear systems to control the conditional Lyapunov exponents of the model system. This method is generalized to address any dynamic system that can be described by a set of ordinary first-order differential equations. The Python programming language is used to develop scripts that take a simple text-file representation of the model vector field and output correctly formatted files for use with readily available optimization software. With the use of these Python scripts, examples of the dynamic state and parameter estimation method are shown for a range of neurobiological models, ranging from simple to highly complicated, using simulated data. In this way, the strengths and weaknesses of this methodology are explored, in order to expand the applicability to complex experimental systems.

Modelling Simulation and Control of Non linear Dynamical Systems

Modelling  Simulation and Control of Non linear Dynamical Systems
Author: Patricia Melin,Oscar Castillo
Publsiher: CRC Press
Total Pages: 262
Release: 2001-10-25
ISBN 10: 1420024523
ISBN 13: 9781420024524
Language: EN, FR, DE, ES & NL

Modelling Simulation and Control of Non linear Dynamical Systems Book Review:

These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la

Dynamic Modelling and Control of National Economies 1986

Dynamic Modelling and Control of National Economies  1986
Author: Béla Martos,Louis-François Pau,M. Ziermann
Publsiher: Pergamon
Total Pages: 494
Release: 1987
ISBN 10:
ISBN 13: UOM:39015013059103
Language: EN, FR, DE, ES & NL

Dynamic Modelling and Control of National Economies 1986 Book Review:

This IFAC symposium considers the modelling, analysis and control of various economic and socio-economic systems. The volume is divided into three sections covering: economic theory; macroeconomic policymaking - national, sectoral and regional models; mathematical, algorithmical and computational methods of modelling, giving a clear and concise view of the use of computer systems in the world of economics.

Computational Strategies for Nonlinear Model Predictive Control

Computational Strategies for Nonlinear Model Predictive Control
Author: Matthew J. Tenny
Publsiher: Unknown
Total Pages: 217
Release: 2002
ISBN 10:
ISBN 13: WISC:89084449313
Language: EN, FR, DE, ES & NL

Computational Strategies for Nonlinear Model Predictive Control Book Review:

Wave and Scattering Methods for Numerical Simulation

Wave and Scattering Methods for Numerical Simulation
Author: Stefan Bilbao
Publsiher: John Wiley & Sons
Total Pages: 380
Release: 2004-10-22
ISBN 10: 0470870184
ISBN 13: 9780470870181
Language: EN, FR, DE, ES & NL

Wave and Scattering Methods for Numerical Simulation Book Review:

Scattering-based numerical methods are increasingly applied to the numerical simulation of distributed time-dependent physical systems. These methods, which possess excellent stability and stability verification properties, have appeared in various guises as the transmission line matrix (TLM) method, multidimensional wave digital (MDWD) filtering and digital waveguide (DWN) methods. This text provides a unified framework for all of these techniques and addresses the question of how they are related to more standard numerical simulation techniques. Covering circuit/scattering models in electromagnetics, transmission line modelling, elastic dynamics, as well as time-varying and nonlinear systems, this book highlights the general applicability of this technique across a variety of disciplines, as well as the inter-relationships between simulation techniques and digital filter design. provides a comprehensive overview of scattering-based numerical integration methods. reviews the basics of classical electrical network theory, wave digital filters, and digital waveguide networks. discusses applications for time-varying and nonlinear systems. includes an extensive bibliography containing over 250 references. Mixing theory and application with numerical simulation results, this book will be suitable for both experts and readers with a limited background in signal processing and numerical techniques.

Optimal Control Systems

Optimal Control Systems
Author: A. A. Feld́baum,Anatoli Torokhti,Phil Howlett
Publsiher: Elsevier Science Limited
Total Pages: 452
Release: 1965
ISBN 10:
ISBN 13: UOM:39015002039694
Language: EN, FR, DE, ES & NL

Optimal Control Systems Book Review:

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

Statistical Decision Theory in Adaptive Control Systems

Statistical Decision Theory in Adaptive Control Systems
Author: Anatoli Torokhti,Yoshikazu Sawaragi
Publsiher: Elsevier Science Limited
Total Pages: 216
Release: 1967
ISBN 10: 9780126203509
ISBN 13: 0126203504
Language: EN, FR, DE, ES & NL

Statistical Decision Theory in Adaptive Control Systems Book Review:

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

Plastic Flow and Fracture in Solids

Plastic Flow and Fracture in Solids
Author: Anatoli Torokhti,Tracy Yerkes Thomas
Publsiher: Elsevier Science Limited
Total Pages: 267
Release: 1961
ISBN 10:
ISBN 13: STANFORD:36105030406818
Language: EN, FR, DE, ES & NL

Plastic Flow and Fracture in Solids Book Review:

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 Structural Mechanics and Dynamics

Computational Methods for Structural Mechanics and Dynamics
Author: Anonim
Publsiher: Unknown
Total Pages: 597
Release: 1989
ISBN 10:
ISBN 13: PSU:000015203342
Language: EN, FR, DE, ES & NL

Computational Methods for Structural Mechanics and Dynamics Book Review: