| Description |
1 online resource (607 pages) |
| Contents |
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Tempora Domains; Copyright; Contents; Preface; 1 Introduction; 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 Nonlinear System Identification; 1.4 NARMAX Methods; 1.5 The NARMAX Philosophy; 1.6 What is System Identification For?; 1.7 Frequency Response of Nonlinear Systems; 1.8 Continuous-Time, Severely Nonlinear, and Time-Varying Models and Systems; 1.9 Spatio-temporal Systems |
|
1.10 Using Nonlinear System Identification in Practice and Case Study ExamplesReferences; 2 Models for Linear and Nonlinear Systems; 2.1 Introduction; 2.2 Linear Models; 2.2.1 Autoregressive Moving Average with Exogenous Input Model; 2.2.1.1 FIR Model; 2.2.1.2 AR Model; 2.2.1.3 MA Model; 2.2.1.4 ARMA Model; 2.2.1.5 ARX Model; 2.2.1.6 ARMAX Model; 2.2.1.7 Box-Jenkins Model; 2.2.2 Parameter Estimation for Linear Models; 2.2.2.1 ARX Model Parameter Estimation -- The Least Squares Algorithm; 2.2.2.2 ARMAX Model Parameter Estimation -- The Extended Least Squares Algorithm |
|
2.3 Piecewise Linear Models2.3.1 Spatial Piecewise Linear Models; 2.3.1.1 Operating Regions; 2.3.1.2 Parameter Estimation; 2.3.1.3 Simulation Example; 2.3.2 Models with Signal-Dependent Parameters; 2.3.2.1 Decomposition of Signal-Dependent Models; 2.3.2.2 Parameter Estimation of Signal-Dependent Models; 2.3.2.3 Simulation Example; 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 |
|
2.6.2.1 Integral Model2.6.2.2 Recursive Model; 2.6.2.3 Output-affine 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; 2.8.2.1 Activation Functions; 2.8.2.2 Radial Basis Function Networks; 2.9 Wavelet Models; 2.9.1 Dynamic Wavelet Models; 2.9.1.1 Random Noise; 2.9.1.2 Coloured Noise; 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 ModelsReferences; 3 Model Structure Detection and Parameter Estimation; 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; 3.3.1.1 The FROLS Algorithm |
| Summary |
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 |
| Notes |
3.3.1.2 Variants of the FROLS Algorithm |
|
Print version record |
| Subject |
Nonlinear systems.
|
|
Nonlinear theories -- Mathematical models
|
|
Systems engineering.
|
|
systems engineering.
|
|
TECHNOLOGY & ENGINEERING -- Quality Control.
|
|
Nonlinear systems
|
|
Nonlinear theories -- Mathematical models
|
|
Systems engineering
|
| Form |
Electronic book
|
| ISBN |
9781118535547 |
|
1118535545 |
|
9781118535554 |
|
1118535553 |
|
