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Edition: Online-Ausg. [s.l.] [Wiley] Online-Ressource ISBN 9781118535530 (MobiPocket)

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Edition: ISBN 1118535561 (electronic bk.)

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ISBN: 9781119943594 , 9781118535530 (Sekundärausgabe) , 1118535537 (Sekundärausgabe) , 9781118535547 (Sekundärausgabe) , 1118535545 (Sekundärausgabe) , 9781118535554 (Sekundärausgabe) , 1118535553 (Sekundärausgabe) , 9781118535561 (Sekundärausgabe) , 1118535561 (Sekundärausgabe)

Note: Includes bibliographical references and index , Online-Ausg.:

Language: English

DOI: 10.1002/9781118535561

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118535561

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Format: X, 192 S. , graph. Darst.

ISBN: 0863410197

Series Statement: Institution of Electrical Engineers: IEE control engineering series. 25.

Note: Literaturangaben

Language: English

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Format: X, 192 S. : ILL., GRAPH. DARST.

Series Statement: IEE CONTROL ENGINEERING SERIES 25

Language: Undetermined

UID:

Format: 1 online resource

ISBN: 9781118535530 , 1118535537 , 9781118535547 , 1118535545 , 9781118535554 , 1118535553 , 9781118535561 , 1118535561 , 1119943590 , 9781119943594

Content: This book helps practitioners and researchers find ways to solve difficult nonlinear system identification problems using the well-established NARMAX method. It is a description of a class of system identification algorithms that can be used to identify nonlinear dynamic models from recorded data. Written with an emphasis on making algorithms and methods accessible so that they can be applied and used in practice, this book also addresses frequency and spatio-temporal methods rarely covered elsewhere, and which can provide significant insights into complex system behaviours.

Note: Introduction to System Identification -- , System Models and Simulation -- , Systems and Signals -- , System Identification -- , Linear System Identification -- , Non-linear System Identification -- , NARMAX Methods -- , The NARMAX Philosophy -- , What is System Identification For? -- , Frequency Response of Non-linear Systems -- , Continuous-Time, Severely Non-linear, and Time-Varying Models and Systems -- , Spatio-temporal Systems -- , Using Non-linear System Identification in Practice and Case Study Examples -- , References -- , Introduction -- , Linear Models -- , Autoregressive Moving Average with Exogenous Input Model -- , Parameter Estimation for Linear Models -- , Piecewise Linear Models -- , Spatial Piecewise Linear Models -- , Models with Signal-Dependent Parameters -- , Remarks on Piecewise Linear Models -- , Volterra Series Models -- , Block-Structured Models -- , Parallel Cascade Models -- , Feedback Block-Structured Models -- , NARMAX Models -- , Polynomial NARMAX Model -- , Rational NARMAX Model -- , The Extended Model Set Representation -- , Generalised Additive Models -- , Neural Networks -- , Multi-layer Networks -- , Single-Layer Networks -- , Wavelet Models -- , Dynamic Wavelet Models -- , State-Space Models -- , Extensions to the MIMO Case -- , Noise Modelling -- , Noise-Free -- , Additive Random Noise -- , Additive Coloured Noise -- , General Noise -- , Spatio-temporal Models -- , References -- , Introduction -- , The Orthogonal Least Squares Estimator and the Error Reduction Ratio -- , Linear-in-the-Parameters Representation -- , The Matrix Form of the Linear-in-the-Parameters Representation -- , The Basic OLS Estimator -- , The Matrix Formulation of the OLS Estimator -- , The Error Reduction Ratio -- , An Illustrative Example of the Basic OLS Estimator -- , The Forward Regression OLS Algorithm -- , Forward Regression with OLS -- , An Illustrative Example of Forward Regression with OLS -- , The OLS Estimation Engine and Identification Procedure -- , Term and Variable Selection -- , OLS and Sum of Error Reduction Ratios -- , Sum of Error Reduction Ratios -- , The Variance of the s-Step-Ahead Prediction Error -- , The Final Prediction Error -- , The Variable Selection Algorithm -- , Noise Model Identification -- , The Noise Model -- , A Simulation Example with Noise Modelling -- , An Example of Variable and Term Selection for a Real Data Set -- , ERR is Not Affected by Noise -- , Common Structured Models to Accommodate Different Parameters -- , Model Parameters as a Function of Another Variable -- , System Internal and External Parameters -- , Parameter-Dependent Model Structure -- , Modelling Auxetic Foams -- An Example of External Parameter-Dependent Model Identification -- , OLS and Model Reduction -- , Recursive Versions of OLS -- , References -- , Introduction -- , Feature Selection and Feature Extraction -- , Principal Components Analysis -- , A Forward Orthogonal Search Algorithm -- , The Basic Idea of the FOS-MOD Algorithm -- , Feature Detection and Ranking -- , Monitoring the Search Procedure -- , Illustrative Examples -- , A Basis-Ranking Algorithm Based on PCA -- , Principal Component-Derived Multiple Regression -- , PCA-Based MFROLS Algorithms -- , An Illustrative Example -- , References -- , Introduction -- , Detection of Non-linearity -- , Estimation and Test Data Sets -- , Model Predictions -- , One-Step-Ahead Prediction -- , Model Predicted Output -- , Statistical Validation -- , Correlation Tests for Input-Output Models -- , Correlation Tests for Time Series Models -- , Correlation Tests for MIMO Models -- , Output-Based Tests -- , Term Clustering -- , Qualitative Validation of Non-linear Dynamic Models -- , Poincaré Sections -- , Bifurcation Diagrams -- , Cell Maps -- , Qualitative Validation in Non-linear System Identification -- , References -- , Introduction -- , Generalised Frequency Response Functions -- , The Volterra Series Representation of Non-linear Systems -- , Generalised Frequency Response Functions -- , The Relationship Between GFRFs and Output Response of Non-linear Systems -- , Interpretation of the Composition of the Output Frequency Response of Non-linear Systems -- , Estimation and Computation of GFRFs -- , The Analysis of Non-linear Systems Using GFRFs -- , Output Frequencies of Non-linear Systems -- , Output Frequencies of Non-linear Systems under Multi-tone Inputs -- , Output Frequencies of Non-linear Systems for General Inputs -- , Non-linear Output Frequency Response Functions -- , Definition and Properties of NOFRFs -- , Evaluation of NOFRFs -- , Damage Detection Using NARMAX Modelling and NOFRF-Based Analysis -- , Output Frequency Response Function of Non-linear Systems -- , Definition of the OFRF -- , Determination of the OFRF -- , Application of the OFRF to Analysis of Non-linear Damping for Vibration Control -- , References -- , Introduction -- , Energy Transfer Filters -- , The Time and Frequency Domain Representation of the NARX Model with Input Non-linearity -- , Energy Transfer Filter Designs -- , Energy Focus Filters -- , Output Frequencies of Non-linear Systems with Input Signal Energy Located in Two Separate Frequency Intervals -- , The Energy Focus Filter Design Procedure and an Example -- , OFRF-Based Approach for the Design of Non-linear Systems in the Frequency Domain -- , OFRF-Based Design of Non-linear Systems in the Frequency Domain -- , Design of Non-linear Damping in the Frequency Domain for Vibration Isolation: An Experimental Study -- , References -- , Introduction -- , The Multi-layered Perceptron -- , Radial Basis Function Networks -- , Training Schemes for RBF Networks -- , Fixed Kernel Centres with a Single Width -- , Limitation of RBF Networks with a Single Kernel Width -- , Fixed Kernel Centres and Multiple Kernel Widths -- , Wavelet Networks -- , Wavelet Decompositions -- , Wavelet Networks -- , Limitations of Fixed Grid Wavelet Networks -- , A New Class of Wavelet Networks -- , Multi-resolution Wavelet Models and Networks -- , Multi-resolution Wavelet Decompositions -- , Multi-resolution Wavelet Models and Networks -- , An Illustrative Example -- , References -- , Introduction -- , Wavelet NARMAX Models -- , Non-linear System Identification Using Wavelet Multi-resolution NARMAX Models -- , A Strategy for Identifying Non-linear Systems -- , Systems that Exhibit Sub-harmonics and Chaos -- , Limitations of the Volterra Series Representation -- , Time Domain Analysis -- , The Response Spectrum Map -- , Introduction -- , Examples of the Response Spectrum Map -- , A Modelling Framework for Sub-harmonic and Severely-Non-linear Systems -- , Input Signal Decomposition -- , MISO NARX Modelling in the Time Domain -- , Frequency Response Functions for Sub-harmonic Systems -- , MISO Frequency Domain Volterra Representation -- , Generating the GFRFs from the MISO Model -- , Analysis of Sub-harmonic Systems and the Cascade to Chaos -- , Frequency Domain Response Synthesis -- , An Example of Frequency Domain Analysis for Sub-harmonic Systems -- , References -- , Introduction -- , The Kernel Invariance Method -- , Definitions -- , Reconstructing the Linear Model Terms -- , Reconstructing the Quadratic Model Terms -- , Model Structure Determination -- , Using the GFRFs to Reconstruct Non-linear Integro-differential Equation Models Without Differentiation -- , Introduction -- , Reconstructing the Linear Model Terms -- , Reconstructing the Quadratic Model Terms -- , Reconstructing the Higher-Order Model Terms -- , A Real Application -- , References -- , Introduction -- , Adaptive Parameter Estimation Algorithms -- , The Kalman Filter Algorithm -- , The RLS and LMS Algorithms -- , Some Practical Considerations for the KF, RLS, and LMS Algorithms -- , Tracking Rapid Parameter Variations Using Wavelets -- , A General Form of TV-ARX Models Using Wavelets -- , A Multi-wavelet Approach for Time-Varying Parameter Estimation -- , Time-Dependent Spectral Characterisation -- , The Definition of a Time-Dependent Spectral Function -- , Non-linear Time-Varying Model Estimation -- , Mapping and Tracking in the Frequency Domain -- , Time-Varying Frequency Response Functions -- , First- and Second-Order TV-GFRFs -- , A Sliding Window Approach -- , References -- , Introduction -- , Cellular Automata -- , History of Cellular Automata -- , Discrete Lattice -- , Neighbourhood -- , Transition Rules -- , Simulation Examples of Cellular Automata. , Identification of Cellular Automata -- , Introduction and Review -- , Polynomial Representation -- , Neighbourhood Detection and Rule Identification -- , N-State Systems -- , Introduction to Excitable Media Systems -- , Simulation of Excitable Media -- , Identification of Excitable Media Using a CA Model -- , General N-State Systems -- , References -- , Introduction -- , Spatio-temporal Patterns and Continuous-State Models -- , Stem Cell Colonies -- , The Belousov-Zhabotinsky Reaction -- , Oxygenation in Brain -- , Growth Patterns -- , A Simulated Example Showing Spatio-temporal Chaos from CML Models -- , Identification of Coupled Map Lattice Models -- , Deterministic CML Models -- , The Identification of Stochastic CML Models -- , Identification of Partial Differential Equation Models -- , Model Structure -- , Time Discretisation -- , Non-linear Function Approximation -- , Non-linear Frequency Response Functions for Spatio-temporal Systems -- , A One-Dimensional Example -- , Higher-Order Frequency Response Functions -- , References -- , Introduction -- , Practical System Identification -- , Characterisation of Robot Behaviour -- , Door Traversal -- , Route Learning -- , System Identification for Space Weather and the Magnetosphere -- , Detecting and Tracking Iceberg Calving in Greenland -- , Causality Detection -- , Results -- , Detecting and Tracking Time-Varying Causality for EEG Data -- , Data Acquisition -- , Causality Detection -- , Detecting Linearity and Non-linearity -- , The Identification and Analysis of Fly Photoreceptors -- , Identification of the Fly Photoreceptor -- , Model-Based System Analysis in the Time and Frequency Domain -- , Real-Time Diffuse Optical Tomography Using RBF Reduced-Order Models of the Propagation of Light for Monitoring Brain Haemodynamics -- , Diffuse Optical Imaging -- , In-vivo Real-Time 3-D Brain Imaging Using Reduced-Order Forward Models -- , Identification of Hysteresis Effects in Metal Rubber Damping Devices -- , Dynamic Modelling of Metal Rubber Damping Devices -- , Model Identification of a Metal Rubber Specimen -- , Identification of the Belousov-Zhabotinsky Reaction -- , Data Acquisition -- , Model Identification -- , Dynamic Modelling of Synthetic Bioparts -- , The Biopart and the Experiments -- , NARMAX Model of the Synthetic Biopart -- , Forecasting High Tides in the Venice Lagoon -- , Time Series Forecasting Problem -- , Water-Level Modelling and High-Tide Forecasting -- , References.

Additional Edition: Print version: Billings, S.A. Nonlinear system identification. Chichester, West Sussex, United Kingdom : John Wiley & Sons, Inc., 2013 ISBN 9781119943594

Language: English

Keywords: Electronic books. ; Electronic books. ; Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118535561

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118535561

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118535561

UID:

Format: X, 192 S. : Ill., graph. Darst.

ISBN: 0863410197

Series Statement: IEE control engineering series 25

Language: English

UID:

Format: MF

Note: Sheffield, Univ., Ph.D.Thesis 1976

Language: Undetermined

UID:

Format: 1 Online-Ressource (1 online resource)

ISBN: 1118535537 , 1118535545 , 1118535553 , 1118535561 , 1119943590 , 9781118535554 , 9781118535530 , 9781118535561 , 9781118535547 , 9781119943594

Content: Machine-generated contents note:1.1.Introduction to System Identification --1.1.1.System Models and Simulation --1.1.2.Systems and Signals --1.1.3.System Identification --1.2.Linear System Identification --1.3.Non-linear System Identification --1.4.NARMAX Methods --1.5.The NARMAX Philosophy --1.6.What is System Identification For? --1.7.Frequency Response of Non-linear Systems --1.8.Continuous-Time, Severely Non-linear, and Time-Varying Models and Systems --1.9.Spatio-temporal Systems --1.10.Using Non-linear System Identification in Practice and Case Study Examples --References --2.1.Introduction --2.2.Linear Models --2.2.1.Autoregressive Moving Average with Exogenous Input Model --2.2.2.Parameter Estimation for Linear Models --2.3.Piecewise Linear Models --2.3.1.Spatial Piecewise Linear Models --2.3.2.Models with Signal-Dependent Parameters --2.3.3.Remarks on Piecewise Linear Models --2.4.Volterra Series Models --2.5.Block-Structured Models --2.5.1.Parallel Cascade Models --2.5.2.Feedback Block-Structured Models --2.6.NARMAX Models --2.6.1.Polynomial NARMAX Model --2.6.2.Rational NARMAX Model --2.6.3.The Extended Model Set Representation --2.7.Generalised Additive Models --2.8.Neural Networks --2.8.1.Multi-layer Networks --2.8.2.Single-Layer Networks --2.9.Wavelet Models --2.9.1.Dynamic Wavelet Models --2.10.State-Space Models --2.11.Extensions to the MIMO Case --2.12.Noise Modelling --2.12.1.Noise-Free --2.12.2.Additive Random Noise --2.12.3.Additive Coloured Noise --2.12.4.General Noise --2.13.Spatio-temporal Models --References --3.1.Introduction --3.2.The Orthogonal Least Squares Estimator and the Error Reduction Ratio --3.2.1.Linear-in-the-Parameters Representation --3.2.2.The Matrix Form of the Linear-in-the-Parameters Representation --3.2.3.The Basic OLS Estimator --3.2.4.The Matrix Formulation of the OLS Estimator --3.2.5.The Error Reduction Ratio --3.2.6.An Illustrative Example of the Basic OLS Estimator --3.3.The Forward Regression OLS Algorithm --3.3.1.Forward Regression with OLS --3.3.2.An Illustrative Example of Forward Regression with OLS --3.3.3.The OLS Estimation Engine and Identification Procedure --3.4.Term and Variable Selection --3.5.OLS and Sum of Error Reduction Ratios --3.5.1.Sum of Error Reduction Ratios --3.5.2.The Variance of the s-Step-Ahead Prediction Error --3.5.3.The Final Prediction Error --3.5.4.The Variable Selection Algorithm --3.6.Noise Model Identification --3.6.1.The Noise Model --3.6.2.A Simulation Example with Noise Modelling --3.7.An Example of Variable and Term Selection for a Real Data Set --3.8.ERR is Not Affected by Noise --3.9.Common Structured Models to Accommodate Different Parameters --3.10.Model Parameters as a Function of Another Variable --3.10.1.System Internal and External Parameters --3.10.2.Parameter-Dependent Model Structure --3.10.3.Modelling Auxetic Foams -- An Example of External Parameter-Dependent Model Identification --3.11.OLS and Model Reduction --3.12.Recursive Versions of OLS --References --4.1.Introduction --4.2.Feature Selection and Feature Extraction --4.3.Principal Components Analysis --4.4.A Forward Orthogonal Search Algorithm --4.4.1.The Basic Idea of the FOS-MOD Algorithm --4.4.2.Feature Detection and Ranking --4.4.3.Monitoring the Search Procedure --4.4.4.Illustrative Examples --4.5.A Basis-Ranking Algorithm Based on PCA --4.5.1.Principal Component-Derived Multiple Regression --4.5.2.PCA-Based MFROLS Algorithms --4.5.3.An Illustrative Example --References --5.1.Introduction --5.2.Detection of Non-linearity --5.3.Estimation and Test Data Sets --5.4.Model Predictions --5.4.1.One-Step-Ahead Prediction --5.4.2.Model Predicted Output --5.5.Statistical Validation --5.5.1.Correlation Tests for Input-Output Models --5.5.2.Correlation Tests for Time Series Models --5.5.3.Correlation Tests for MIMO Models --5.5.4.Output-Based Tests --5.6.Term Clustering --5.7.Qualitative Validation of Non-linear Dynamic Models --5.7.1.Poincaré Sections --5.7.2.Bifurcation Diagrams --5.7.3.Cell Maps --5.7.4.Qualitative Validation in Non-linear System Identification --References --6.1.Introduction --6.2.Generalised Frequency Response Functions --6.2.1.The Volterra Series Representation of Non-linear Systems --6.2.2.Generalised Frequency Response Functions --6.2.3.The Relationship Between GFRFs and Output Response of Non-linear Systems --6.2.4.Interpretation of the Composition of the Output Frequency Response of Non-linear Systems --6.2.5.Estimation and Computation of GFRFs --6.2.6.The Analysis of Non-linear Systems Using GFRFs --6.3.Output Frequencies of Non-linear Systems --6.3.1.Output Frequencies of Non-linear Systems under Multi-tone Inputs --6.3.2.Output Frequencies of Non-linear Systems for General Inputs --6.4.Non-linear Output Frequency Response Functions --6.4.1.Definition and Properties of NOFRFs --6.4.2.Evaluation of NOFRFs --6.4.3.Damage Detection Using NARMAX Modelling and NOFRF-Based Analysis --6.5.Output Frequency Response Function of Non-linear Systems --6.5.1.Definition of the OFRF --6.5.2.Determination of the OFRF --6.5.3.Application of the OFRF to Analysis of Non-linear Damping for Vibration Control --References --7.1.Introduction --7.2.Energy Transfer Filters --7.2.1.The Time and Frequency Domain Representation of the NARX Model with Input Non-linearity --7.2.2.Energy Transfer Filter Designs --7.3.Energy Focus Filters --7.3.1.Output Frequencies of Non-linear Systems with Input Signal Energy Located in Two Separate Frequency Intervals --7.3.2.The Energy Focus Filter Design Procedure and an Example --7.4.OFRF-Based Approach for the Design of Non-linear Systems in the Frequency Domain --7.4.1.OFRF-Based Design of Non-linear Systems in the Frequency Domain --7.4.2.Design of Non-linear Damping in the Frequency Domain for Vibration Isolation: An Experimental Study --References --8.1.Introduction --8.2.The Multi-layered Perceptron --8.3.Radial Basis Function Networks --8.3.1.Training Schemes for RBF Networks --8.3.2.Fixed Kernel Centres with a Single Width --8.3.3.Limitation of RBF Networks with a Single Kernel Width --8.3.4.Fixed Kernel Centres and Multiple Kernel Widths --8.4.Wavelet Networks --8.4.1.Wavelet Decompositions --8.4.2.Wavelet Networks --8.4.3.Limitations of Fixed Grid Wavelet Networks --8.4.4.A New Class of Wavelet Networks --8.5.Multi-resolution Wavelet Models and Networks --8.5.1.Multi-resolution Wavelet Decompositions --8.5.2.Multi-resolution Wavelet Models and Networks --8.5.3.An Illustrative Example --References --9.1.Introduction --9.2.Wavelet NARMAX Models --9.2.1.Non-linear System Identification Using Wavelet Multi-resolution NARMAX Models --9.2.2.A Strategy for Identifying Non-linear Systems --9.3.Systems that Exhibit Sub-harmonics and Chaos --9.3.1.Limitations of the Volterra Series Representation --9.3.2.Time Domain Analysis --9.4.The Response Spectrum Map --9.4.1.Introduction --9.4.2.Examples of the Response Spectrum Map --9.5.A Modelling Framework for Sub-harmonic and Severely-Non-linear Systems --9.5.1.Input Signal Decomposition --9.5.2.MISO NARX Modelling in the Time Domain --9.6.Frequency Response Functions for Sub-harmonic Systems --9.6.1.MISO Frequency Domain Volterra Representation --9.6.2.Generating the GFRFs from the MISO Model --9.7.Analysis of Sub-harmonic Systems and the Cascade to Chaos --9.7.1.Frequency Domain Response Synthesis --9.7.2.An Example of Frequency Domain Analysis for Sub-harmonic Systems --References --10.1.Introduction --10.2.The Kernel Invariance Method --10.2.1.Definitions --10.2.2.Reconstructing the Linear Model Terms --10.2.3.Reconstructing the Quadratic Model Terms --10.2.4.Model Structure Determination --10.3.Using the GFRFs to Reconstruct Non-linear Integro-differential Equation Models Without Differentiation --10.3.1.Introduction --10.3.2.Reconstructing the Linear Model Terms --10.3.3.Reconstructing the Quadratic Model Terms --10.3.4.Reconstructing the Higher-Order Model Terms --10.3.5.A Real Application --References --11.1.Introduction --11.2.Adaptive Parameter Estimation Algorithms --11.2.1.The Kalman Filter Algorithm --11.2.2.The RLS and LMS Algorithms --11.2.3.Some Practical Considerations for the KF, RLS, and LMS Algorithms --11.3.Tracking Rapid Parameter Variations Using Wavelets --11.3.1.A General Form of TV-ARX Models Using Wavelets --11.3.2.A Multi-wavelet Approach for Time-Varying Parameter Estimation --11.4.Time-Dependent Spectral Characterisation --11.4.1.The Definition of a Time-Dependent Spectral Function --11.5.Non-linear Time-Varying Model Estimation --11.6.Mapping and Tracking in the Frequency Domain --11.6.1.Time-Varying Frequency Response Functions --11.6.2.First- and Second-Order TV-GFRFs --11.7.A Sliding Window Approach --References --12.1.Introduction --12.2.Cellular Automata --12.2.1.History of Cellular Automata --12.2.2.Discrete Lattice --12.2.3.Neighbourhood --12.2.4.Transition Rules --12.2.5.Simulation Examples of Cellular Automata.

Content: Note continued:12.3.Identification of Cellular Automata --12.3.1.Introduction and Review --12.3.2.Polynomial Representation --12.3.3.Neighbourhood Detection and Rule Identification --12.4.N-State Systems --12.4.1.Introduction to Excitable Media Systems --12.4.2.Simulation of Excitable Media --12.4.3.Identification of Excitable Media Using a CA Model --12.4.4.General N-State Systems --References --13.1.Introduction --13.2.Spatio-temporal Patterns and Continuous-State Models --13.2.1.Stem Cell Colonies --13.2.2.The Belousov-Zhabotinsky Reaction --13.2.3.Oxygenation in Brain --13.2.4.Growth Patterns --13.2.5.A Simulated Example Showing Spatio-temporal Chaos from CML Models --13.3.Identification of Coupled Map Lattice Models --13.3.1.Deterministic CML Models --13.3.2.The Identification of Stochastic CML Models --13.4.Identification of Partial Differential Equation Models --13.4.1.Model Structure --13.4.2.Time Discretisation --13.4.3.Non-linear Function Approximation --13.5.Non-linear Frequency Response Functions for Spatio-temporal Systems --13.5.1.A One-Dimensional Example --13.5.2.Higher-Order Frequency Response Functions --References --14.1.Introduction --14.2.Practical System Identification --14.3.Characterisation of Robot Behaviour --14.3.1.Door Traversal --14.3.2.Route Learning --14.4.System Identification for Space Weather and the Magnetosphere --14.5.Detecting and Tracking Iceberg Calving in Greenland --14.5.1.Causality Detection --14.5.2.Results --14.6.Detecting and Tracking Time-Varying Causality for EEG Data --14.6.1.Data Acquisition --14.6.2.Causality Detection --14.6.3.Detecting Linearity and Non-linearity --14.7.The Identification and Analysis of Fly Photoreceptors --14.7.1.Identification of the Fly Photoreceptor --14.7.2.Model-Based System Analysis in the Time and Frequency Domain --14.8.Real-Time Diffuse Optical Tomography Using RBF Reduced-Order Models of the Propagation of Light for Monitoring Brain Haemodynamics --14.8.1.Diffuse Optical Imaging --14.8.2.In-vivo Real-Time 3-D Brain Imaging Using Reduced-Order Forward Models --14.9.Identification of Hysteresis Effects in Metal Rubber Damping Devices --14.9.1.Dynamic Modelling of Metal Rubber Damping Devices --14.9.2.Model Identification of a Metal Rubber Specimen --14.10.Identification of the Belousov-Zhabotinsky Reaction --14.10.1.Data Acquisition --14.10.2.Model Identification --14.11.Dynamic Modelling of Synthetic Bioparts --14.11.1.The Biopart and the Experiments --14.11.2.NARMAX Model of the Synthetic Biopart --14.12.Forecasting High Tides in the Venice Lagoon --14.12.1.Time Series Forecasting Problem --14.12.2.Water-Level Modelling and High-Tide Forecasting --References.

Content: This book helps practitioners and researchers find ways to solve difficult nonlinear system identification problems using the well-established NARMAX method. It is a description of a class of system identification algorithms that can be used to identify nonlinear dynamic models from recorded data. Written with an emphasis on making algorithms and methods accessible so that they can be applied and used in practice, this book also addresses frequency and spatio-temporal methods rarely covered elsewhere, and which can provide significant insights into complex system behaviours

Note: Includes bibliographical references and index

Additional Edition: 9781119943594

Additional Edition: Erscheint auch als Druck-Ausgabe Billings, S.A Nonlinear system identification Chichester, West Sussex, United Kingdom : John Wiley & Sons, Inc., 2013

Language: English

DOI: 10.1002/9781118535561

URL: lizenzpflichtig

URL: lizenzpflichtig

URL: Inhaltstext

UID:

Format: 1 online resource (xvii, 555 p.) : , col. ill., photographs, graphs.

Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

Language: English

Keywords: Electronic books.

URL: Click to View

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ISSN: 0277-6693

Note: In: Journal of forecasting

In: Journal of forecasting, Chichester : Wiley, 1982, 18(1999), 2, Seite 139-149, 0277-6693

In: volume:18

In: year:1999

In: number:2

In: pages:139-149

Language: English

Keywords: Aufsatz in Zeitschrift

UID:

Format: S. 193 - 199

Series Statement: [Research report / Department of Control Engineering, University of Sheffield] [196]

In: IEE proceedings ; 130.1983,4

Language: English