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MATLAB SYSTEM IDENTIFICATION TOOLBOX 7 Handbücher

Bedienungsanleitungen und Benutzerhandbücher für Software MATLAB SYSTEM IDENTIFICATION TOOLBOX 7.
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Matlab SYSTEM IDENTIFICATION TOOLBOX 7 Betriebsanweisung (531 Seiten)


Marke: Matlab | Kategorie: Software | Größe: 4.20 MB |

 

Inhaltsverzeichnis

User’s Guide

1

508-647-7000 (Phone)

2

508-647-7001 (Fax)

2

The MathWorks, Inc

2

3 Apple Hill Drive

2

Natick, MA 01760-2098

2

Revision History

3

About the Developers

5

Linear Model Identification

10

Modeling)

15

Model Analysis

17

Simulation and Prediction

19

Contents

22

Types of Data You Can Model

27

Sampling Intervals

28

- For a data set with N

29

SamplingInstants

30

Ys GsUs() () ()=

33

Representing Data in the GUI

35

Importing Time

37

Domain Data into the GUI

37

0. For more information

41

Prerequisite

43

Specifying

51

Creating Multie

55

Extracting

59

Set into a New Data Set

59

Viewing Data Properties

62

Information About the Data

64

Distinguishing

65

Data Types i n the GUI

65

Organizing Data Icons

65

Subreferencing iddata Objects

77

Naming, Adding

85

Concatenating i

87

Concatenating idfrd Objects

93

Supported Data Plots

97

How to Plot Data in the GUI

98

Working with a Time Plot

99

1 Data Processing

100

Selecting Subsets of Data

108

Selecting Data U

109

Handling Outliers

113

See Also

115

When Not to Detrend Data

117

For example, Ynew = Ysim+y0

121

Resampling Data

122

Using the GUI

123

Resampling

123

Data at the Command Line

123

Filtering D ata

129

Filtering Data

131

Simple Passband Filter

133

Causal and Noncausal Filters

135

Transforming Time-Domain Data

141

Data Processing

146

Transform

149

Choosing Your System

155

Identification Strategy

155

Continuous-Time Models

158

Discrete-Time Models

158

ODEs (Grey-Box Models)

158

Nonlinear Models

159

Nonlinear Black-Box Models

160

Commands for Model Estimation

163

Model Proper ties

168

- Algorithm

169

- EstimationIn fo

170

3 Linear Model Identification

180

Yz GzUz() () ()=

180

Data Suppor ted b

181

How to Estimate

181

How to Estimate F

183

Command Line

183

What Is Frequency Resolution?

185

Spectral Analysis Algorithm

186

Linear Model Identification

188

() ( )=−

193

Step Respo nse Plot

197

Gqut gkut k

198

()() ()( )=−

198

What Is a Process Model?

200

All real or

203

Underdamped

203

Options for Initial States

217

Polynomial Model Structure

220

Ty pes of Supported Data

226

Input Delays

227

[1:10 1:10 1:10]

229

2 and that there is a good

233

Number of parameters =+nn

234

Before You Begin

235

None. Skipping uncertainty

237

Aqyt B qu t nk Cqet

245

()() () ()()=−

245

For example:

249

What Are State-Space Models?

251

Discrete-Time Representation

253

State Matrices

253

Data Supported b

255

Why Estimate Model Orders?

257

State Space: n [n k]

263

4[11]for a fourth-order

263

Supported State-Space Models

265

Parameterization

269

Structured Parameterization?

274

When to Refine Models

281

Guesses at the Command Line

285

Representations

290

Effects on the Noise Model

293

Subreferencing Model Objects

297

Concatenating Model Objects

302

Merging Model Objects

306

Nonlinear Black-Box Model

307

Identification

307

and linear trends

308

Using Regressors

312

1 because the prediction of

313

2), y ou

313

Using Custom Regressors

314

None for t he Nonlinearity

315

How to Estimate N

316

General nlarx Syntax

318

Ha mmerstein-Wiener

324

TypesofNonlinearityEstimators

331

Hammerstein-Wi

337

Nonlinear M odel?

339

Models for a Given Input

340

ODE Parameter Estimation

345

(Grey-Box Modeling)

345

Supported Grey-Box Models

346

Model for Heat Diffusion

353

Model = pem(data,Minit)

359

Simulation Method

364

Search Method

364

Gradient Options

365

Time Series Model

369

What Are Time-Series Models?

370

Preparing Time-Series Data

371

Linear or Logarithmic

372

Linear option is available

372

Estimating AR and ARMA Models

375

ARMA:[na nc]

376

Ny-by-Ny

376

Recursive Techniques for

385

Model Identi fication

385

What Is Recursive Estimation?

386

Kalman Filter Algorithm

392

Forg etting Factor Algorithm

394

0.97 and 0.995

395

0.9 7 to 0.995

395

Algorithm

396

Data Segmentation

398

8 Model Analysis

400

Plotting Models in the GUI

403

Active model

404

Inactive model

404

Plots the model

404

Getting Advice A

405

Supported Model Types

415

How to Plot Resid

419

Example – Ex

419

OutputName is the name of the

427

0 and the

427

What Is Frequency Response?

430

How to Plo

433

[1:.1:100]

435

Creating Noise-Spectrum Plots

438

Supported Models

445

Reducing Model O

449

Definition of FPE

459

Computing FPE

460

Definition of AIC

460

Computing AIC

461

Computing Model Uncertainty

462

Troubleshooting Models

465

Substantial Noi

467

Unstable M

467

Complicated Nonlinearities

469

9 Simulation and Prediction

474

Model at the Comm

478

479

Specifying Initial States

480

Using Identified Models in

483

Control Design

483

Identified Models

484

Using balred to R

485

Compensator D

485

Software

485

What Is the LTI Viewer?

487

Combining Model Objects

488

Using System Identification

491

Toolbox Blocks

491

Preparing Data

494

Identifying L inear Models

495

Simulating Model Output

496

Using the System

501

Identification Tool GUI

501

Starting a New Se

504

Tool GUI

505

Opening a Saved Session

506

Deleting a Session

507

Getting Help in the GUI

507

Managing Models in the GUI

509

Viewing Model Pr

510

Renaming Models

511

Organizing

511

Model Icons

511

Deleting Models in the GUI

512

Workspace

513

Changing

516

Setting Axis Limits

517

Grid, Line Style

519

Opening a Plo

519

Printing Plots

520

TypesofGUICustomization

521

Saving Session Preferences

521

Modifying idlayout.m

522

levels 1-94

529




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