Useful information can be found on this page.


 

Useful Links: 

Updated on Link / Description

 

2018-09-26

 

 

2017-11-14

 

Wind data worldwide:

https://www.ecmwf.int/
 

The following link discusses the way we choose different model structures like ARX, ARMAX, …

http://www.ni.com/white-paper/4028/en/

2017-10-10 Input-Output data gathered from a DC motor speed control system. You need to run the m-file included in the following zip file:

 

Reference: Data sent by Mr. M. Javad Golchi

2017-10-09 The following figure represents pole placement in two scenarios when controller pole is close to the imaginary axis and when it is far from that axis. It is seen that to have far poles from the axis control signal will be greater which might cause practical issues.

 

 

 

 

2017-10-06 Filter Design by Matlab:

 

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2017-10-03 Useful information about the first order and second order systems.
2017-10-02 A useful system Identification GUI in Matlab here
2017-10-01 System Identification open course in MIT here
2017-09-31 The following links offer useful information and data for modeling:

 

 

 

 

 

 

 

 

 

 

 

 

National Climatic Data Center

ِDaisy data


Useful Videos:

A few interesting videos are in the following:


References: 

1. System Identification: Theory for the User (2nd Edition) by Lennart Ljung is one of the most important references of this aspect with the following table of contents for (1999 edition):

 1. Introduction.

PART I. SYSTEMS AND MODELS.

 2. Time-Invariant Linear Systems.

 3. Simulation, Prediction, and Control.

 4. Models of Linear Time-Invariant Systems.

 5. Models for Time-Varying and Nonlinear Systems.

PART II. METHODS.

 6. Nonparametric Time- and Frequency-Domain Methods.

 7.Parameter Estimation Methods.

 8.Convergence and Consistency.

 9. Asymptotic Distribution of Parameter Estimates.

10. Computing the Estimate.

11. Recursive Estimation Methods.

PART III. USER'S CHOICES.

12. Options and Objectives.

13. Affecting the Bias Distribution of Transfer-Function Estimates.

14. Experiment Design.

15. Choice of Identification Criterion.

16. Model Structure Selection and Model Validation.

17. System Identification in Practice.

Appendix I. Some Concepts from Probability Theory.

Appendix II. Some Statistical Techniques for Linear Regressions.


2. Nonlinear System Identification From Classical Approaches to Neural Networks and Fuzzy Models

Author: Oliver Nelles in 2001.

Though it is written for the nonlinear systems, in chapter 16 you may find very interesting aspects of the linear systems.