实例介绍
【实例简介】
系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！
Revision History pril 1988 First printing July 1991 Second printing M ay1995 Third printing November 2000 Fourth printing Revised for Version 5.0(Release 12) pril 2001 Fifth printing July 2002 Online only Revised for Version 5.0.2 Release 13) June 2004 Sixth printing Revised for Version 6.0.1(Release 14) March 2005 Online only Revised for Version 6.1.1Release 14SP2) September 2005 Seventh printing Revised for Version 6.1.2(Release 14SP3) March 2006 Online only Revised for Version 6.1.3(Release 2006a) September 2006 Online only Revised for Version 6.2 Release 2006b) March 2007 Online only Revised for Version 7.0 ( Release 2007a) September 2007 Online only Revised for Version 7.1 (Release 2007b March 2008 Online only Revised for Version 7.2(Release 2008a) October 2008 Online only Revised for Version 7.2.1 Release 2008b) March 2009 Online only Revised for Version 7.3(Release 2009a) September 2009 Online only Revised for Version 7.3.1(Release 2009b) March 2010 Online only Revised for Version 7. 4 (Release 2010a) eptember 2010 Online only Revised for Version 7.4.1(Release 2010b) pril 2011 Online onl Revised for Version 7.4.2(Release 2011a) September 2011 Online only Revised for Version 7.4.3(Release 2011b) March 2012 Online only Revised for Version 8.0( Release 2012a about the Developers About the Developers ystem Identification Toolbox software is developed in association with the following leading researchers in the system identification field Lennart Ljung. Professor Lennart Ljung is with the department of Electrical Engineering at Linkoping University in Sweden. He is a recognized leader in system identification and has published numerous papers and books in this area Qinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut National de recherche en Informatique et en Automatique(INria) and at Institut de Recherche en Informatique et systemes Aleatoires (Irisa), both in rennes France. He conducts research in the areas of nonlinear system identification fault diagnosis, and signal processing with applications in the fields of energy automotive, and biomedical systems Peter Lindskog. Dr. Peter Lindskog is employed by nira dynami AB, Sweden. He conducts research in the areas of system identification signal processing, and automatic control with a focus on vehicle industry applications Anatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire Jean Kuntzmann at the Universite Joseph Fourier, Grenoble, france. He conducts research in the areas of nonparametric statistics, system identification, and stochastic optimization About the developers Contents Choosing Your System Identification Approach Linear model structures 1-2 What Are Model objects? Model objects represent linear systems About model data 1-5 Types of Model objects Dynamic System Models 1-9 Numeric Models 1-11 umeric Linear Time Invariant (LTD Models 1-11 Identified LTI models Identified Nonlinear models 1-12 Nonlinear model structures 1-13 Recommended Model Estimation Sequence 1-14 Supported Models for Time- and Frequency-Domain Data ,,,,,,,1-16 Supported Models for Time-Domain Data 1-16 Supported Models for Frequency-Domain Data 1-17 See also 1-18 Supported Continuous-and Discrete-Time Models 1-19 Model estimation commands 1-21 Creating Model Structures at the command Line ... 1-22 about system Identification Toolbox Model Objects ... 1-22 When to Construct a Model Structure Independently of Estimation 1-23 Commands for Constructing Model Structures 1-24 Model Properties 1-25 See als 1-27 Modeling Multiple-Output Systems ......... 1-28 About Modeling multiple-Output Systems 1-28 Modeling Multiple Outputs Directly 1-29 Modeling multiple outputs as a Combination of Single-Output Models....... 1-29 Improving Multiple-Output Estimation Results by Weighing Outputs During Estimation ....... 1-30 Identified linear Time-Invariant models 1-32 IDLTI Models 1-32 Configuration of the Structure of Measured and Noise o Representation of the Measured and noise Components fo Various model Types 1-33 Components .... 1-35 Imposing Constraints on the Values of Mode Parameters 1-37 Estimation of Linear models 1-8 Data Import and Processing 2「 Supported Data ... 2-3 Ways to Obtain Identification Data Ways to Prepare Data for System Identification ... 2-6 Requirements on Data Sampling Representing Data in MATLAB Workspace ····· Time-Domain Data Representation 2-9 Time-Series Data Representation 2-10 Contents Frequency-Domain Data Representation ....... 2-11 Importing Data into the Gui 2-17 Types of Data You Can import into the GUi 2-17 Importing time-Domain Data into the GUI 2-18 Importing Frequency-Domain Data into the GUI 2-22 Importing Data Objects into the GUI ......... 2-30 Specifying the data sampling interval 2-34 Specifying estimation and validation Data 2-35 Prep ing data Using Quick Start Creating Data Sets from a Subset of Signal Channelo 2-36 2-37 Creating multiexperiment Data Sets in the gUi 2-39 Managing data in the gui ............. 2-46 Representing Time- and Frequency-Domain Data Using iddata object 2-55 iddata constructor 2-55 iddata Properties......... 2-58 Creating Multiexperiment Data at the Command Line .. 2-61 Select Data Channels, I/O Data and Experiments in iddata Objects 2-63 Increasing Number of Channels or Data Points of iddata Objects 2-67 Managing iddata Objects 2-69 Representing Frequency-Response Data Using idfrd Obiec 2-76 idfrd Constructor 2-76 idfrd Properties 2-77 Select I/o Channels and Data in idfrd Objects ..... 2-79 Adding Input or Output Channels in idfrd Objects 2-80 Managing idfrd Objects 2-83 Operations That Create idfrd Objects 2-83 Analyzing Data quality 2-85 Is your data ready for modeling? 2-85 Plotting Data in the guI Versus at the command line 2-86 How to plot data in the gui 2-86 How to plot data at the command line 2-92 How to Analyze Data Using the advice Command 2-94 Selecting Subsets of Data 2-96 IX Why Select Subsets of Data? 2-96 Extract Subsets of Data Using the GUI 2-97 Extract Subsets of data at the Command Line 2-99 Handling Missing Data and outliers 2-100 Handling missing data 2-100 Handling outliers 2-101 Extract and Model Specific Data Segments 2-102 See also 2-103 Handling offsets and Trends in Data 2-104 When to detrend data 2-104 Alternatives for Detrending Data in GUi or at the Command-Line 2-105 Next Steps After detrending 2-107 How to Detrend Data Using the Gui 2-108 How to detrend data at the Command line 2-109 Detrending Steady-State Dat 109 cending transient Dat 2-109 See also 2-110 Resampling Data 2-111 What Is resampling?...,,.,,,,,,,,,,,.2-111 Resampling data without Aliasing Effects 2-112 See also 2-116 Resampling data Using the GUi .,,,,2-117 Resampling Data at the Command line 2-118 Filtering Data 2-120 Supported Filters 2-120 Choosing to Prefilter Your Data 2-120 See also 2-121 How to Filter Data Using the gui 2-122 Filtering Time-Domain Data in the GuI........ 2-122 Content 【实例截图】
【核心代码】
系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！
Revision History pril 1988 First printing July 1991 Second printing M ay1995 Third printing November 2000 Fourth printing Revised for Version 5.0(Release 12) pril 2001 Fifth printing July 2002 Online only Revised for Version 5.0.2 Release 13) June 2004 Sixth printing Revised for Version 6.0.1(Release 14) March 2005 Online only Revised for Version 6.1.1Release 14SP2) September 2005 Seventh printing Revised for Version 6.1.2(Release 14SP3) March 2006 Online only Revised for Version 6.1.3(Release 2006a) September 2006 Online only Revised for Version 6.2 Release 2006b) March 2007 Online only Revised for Version 7.0 ( Release 2007a) September 2007 Online only Revised for Version 7.1 (Release 2007b March 2008 Online only Revised for Version 7.2(Release 2008a) October 2008 Online only Revised for Version 7.2.1 Release 2008b) March 2009 Online only Revised for Version 7.3(Release 2009a) September 2009 Online only Revised for Version 7.3.1(Release 2009b) March 2010 Online only Revised for Version 7. 4 (Release 2010a) eptember 2010 Online only Revised for Version 7.4.1(Release 2010b) pril 2011 Online onl Revised for Version 7.4.2(Release 2011a) September 2011 Online only Revised for Version 7.4.3(Release 2011b) March 2012 Online only Revised for Version 8.0( Release 2012a about the Developers About the Developers ystem Identification Toolbox software is developed in association with the following leading researchers in the system identification field Lennart Ljung. Professor Lennart Ljung is with the department of Electrical Engineering at Linkoping University in Sweden. He is a recognized leader in system identification and has published numerous papers and books in this area Qinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut National de recherche en Informatique et en Automatique(INria) and at Institut de Recherche en Informatique et systemes Aleatoires (Irisa), both in rennes France. He conducts research in the areas of nonlinear system identification fault diagnosis, and signal processing with applications in the fields of energy automotive, and biomedical systems Peter Lindskog. Dr. Peter Lindskog is employed by nira dynami AB, Sweden. He conducts research in the areas of system identification signal processing, and automatic control with a focus on vehicle industry applications Anatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire Jean Kuntzmann at the Universite Joseph Fourier, Grenoble, france. He conducts research in the areas of nonparametric statistics, system identification, and stochastic optimization About the developers Contents Choosing Your System Identification Approach Linear model structures 1-2 What Are Model objects? Model objects represent linear systems About model data 1-5 Types of Model objects Dynamic System Models 1-9 Numeric Models 1-11 umeric Linear Time Invariant (LTD Models 1-11 Identified LTI models Identified Nonlinear models 1-12 Nonlinear model structures 1-13 Recommended Model Estimation Sequence 1-14 Supported Models for Time- and Frequency-Domain Data ,,,,,,,1-16 Supported Models for Time-Domain Data 1-16 Supported Models for Frequency-Domain Data 1-17 See also 1-18 Supported Continuous-and Discrete-Time Models 1-19 Model estimation commands 1-21 Creating Model Structures at the command Line ... 1-22 about system Identification Toolbox Model Objects ... 1-22 When to Construct a Model Structure Independently of Estimation 1-23 Commands for Constructing Model Structures 1-24 Model Properties 1-25 See als 1-27 Modeling Multiple-Output Systems ......... 1-28 About Modeling multiple-Output Systems 1-28 Modeling Multiple Outputs Directly 1-29 Modeling multiple outputs as a Combination of Single-Output Models....... 1-29 Improving Multiple-Output Estimation Results by Weighing Outputs During Estimation ....... 1-30 Identified linear Time-Invariant models 1-32 IDLTI Models 1-32 Configuration of the Structure of Measured and Noise o Representation of the Measured and noise Components fo Various model Types 1-33 Components .... 1-35 Imposing Constraints on the Values of Mode Parameters 1-37 Estimation of Linear models 1-8 Data Import and Processing 2「 Supported Data ... 2-3 Ways to Obtain Identification Data Ways to Prepare Data for System Identification ... 2-6 Requirements on Data Sampling Representing Data in MATLAB Workspace ····· Time-Domain Data Representation 2-9 Time-Series Data Representation 2-10 Contents Frequency-Domain Data Representation ....... 2-11 Importing Data into the Gui 2-17 Types of Data You Can import into the GUi 2-17 Importing time-Domain Data into the GUI 2-18 Importing Frequency-Domain Data into the GUI 2-22 Importing Data Objects into the GUI ......... 2-30 Specifying the data sampling interval 2-34 Specifying estimation and validation Data 2-35 Prep ing data Using Quick Start Creating Data Sets from a Subset of Signal Channelo 2-36 2-37 Creating multiexperiment Data Sets in the gUi 2-39 Managing data in the gui ............. 2-46 Representing Time- and Frequency-Domain Data Using iddata object 2-55 iddata constructor 2-55 iddata Properties......... 2-58 Creating Multiexperiment Data at the Command Line .. 2-61 Select Data Channels, I/O Data and Experiments in iddata Objects 2-63 Increasing Number of Channels or Data Points of iddata Objects 2-67 Managing iddata Objects 2-69 Representing Frequency-Response Data Using idfrd Obiec 2-76 idfrd Constructor 2-76 idfrd Properties 2-77 Select I/o Channels and Data in idfrd Objects ..... 2-79 Adding Input or Output Channels in idfrd Objects 2-80 Managing idfrd Objects 2-83 Operations That Create idfrd Objects 2-83 Analyzing Data quality 2-85 Is your data ready for modeling? 2-85 Plotting Data in the guI Versus at the command line 2-86 How to plot data in the gui 2-86 How to plot data at the command line 2-92 How to Analyze Data Using the advice Command 2-94 Selecting Subsets of Data 2-96 IX Why Select Subsets of Data? 2-96 Extract Subsets of Data Using the GUI 2-97 Extract Subsets of data at the Command Line 2-99 Handling Missing Data and outliers 2-100 Handling missing data 2-100 Handling outliers 2-101 Extract and Model Specific Data Segments 2-102 See also 2-103 Handling offsets and Trends in Data 2-104 When to detrend data 2-104 Alternatives for Detrending Data in GUi or at the Command-Line 2-105 Next Steps After detrending 2-107 How to Detrend Data Using the Gui 2-108 How to detrend data at the Command line 2-109 Detrending Steady-State Dat 109 cending transient Dat 2-109 See also 2-110 Resampling Data 2-111 What Is resampling?...,,.,,,,,,,,,,,.2-111 Resampling data without Aliasing Effects 2-112 See also 2-116 Resampling data Using the GUi .,,,,2-117 Resampling Data at the Command line 2-118 Filtering Data 2-120 Supported Filters 2-120 Choosing to Prefilter Your Data 2-120 See also 2-121 How to Filter Data Using the gui 2-122 Filtering Time-Domain Data in the GuI........ 2-122 Content 【实例截图】
【核心代码】
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