Identificazione dei Modelli ed Analisi dei Dati
Modulo di 56 ore per il III anno della Laurea
in Ingegneria dell'Automazione e per la Specialistica in Ingegneria dell'Automazione (N.O.) al Dipartimento di
Ingegneria dell'Università di Ferrara. A.A. 2006/2007.
Course Programme
-
Lecture 1. Introduction - General introduction to modelling and
system identification. (i) Theory and experiment based modelling
methods; (ii) Parametric and non-parametric models and identification
methods; (iii) Procedure of system identification. Reading: L. Ljung,
From Data to Model: A Guided Tour of System Identification, Linköping
University, Sweden, Report No. LiTH-ISY-R-1652, 1994. Lecture Notes:
Lecture 1. (Lesson Slides
PDF Format, 4MB); (Lesson Slides PDF
Zipped Format, 3.5MB). (Lesson Slides PDF,
2 slides per page, 4MB).
-
Lecture 2. Non-recursive (off-line) methods. (i) Least-Squares
(LS) method and its variants; (ii) Instrumental variable methods;
(iii) Prediction error methods. Lecture Notes: Lecture 2.
(Lesson Slides PDF Format, 2.5MB); (Lesson Slides PDF Zipped
Format, 2MB). (Lesson Slides PDF,
2 slides per page, 2MB).
-
Lecture 2.5. Nonparametric Identification. Input Signals for
Identification. Identification Conditions. Lecture Notes:
Lecture 2.5. (Lesson Slides PDF Format, 1.6MB); (Lesson Slides PDF
Zipped Format, 1.4MB). (Lesson Slides PDF,
2 slides per page, 1.4MB).
-
Lecture 3. Recursive (on-line) methods. (i) Recursive
Least-Squares (RLS) methods; (ii) Tacking and forgetting factor
techniques. Lecture Notes: Lecture 3.
(Lesson Slides PDF Format, 1.5MB); (Lesson Slides PDF Zipped
Format, 1.2MB). (Lesson Slides PDF,
2 slides per page, 1.2MB).
-
Lecture 5.5. Remarks on Persistent Excitation
Conditions. Practical Aspects. Lecture Notes:
Lecture 5.5.
(Lesson Slides PDF Format, 0.4MB); (Lesson Slides PDF Zipped
Format, 0.38MB). (Lesson Slides PDF,
2 slides per page, 0.38MB).
-
Lecture 4.6. Model Structure Determination. Model
Validation. Theoretic Results and Practical Examples. Lecture Notes:
Lecture 4.6.
(Lesson Slides PDF Format, 2.6MB); (Lesson Slides PDF Zipped
Format, 2.3MB). (Lesson Slides PDF,
2 slides per page, 2.3MB).
-
Lecture 4. PEM Identification Method. Minimisation Techniques
and Numerical Algorithms. Lecture Notes: Lecture 4.
(Lesson Slides PDF Format, 1.5MB); (Lesson Slides PDF Zipped
Format, 1.2MB). (Lesson Slides PDF,
2 slides per page, 2MB).
-
Lecture 4.5. Instrumental Variable Method (IVM). Instrument Theory
and Numerical Implementation Algorithms. Lecture Notes: Lecture 4.5.
(Lesson Slides PDF Format, 1.7MB); (Lesson Slides PDF Zipped
Format, 1.5MB). (Lesson Slides PDF,
2 slides per page, 1.6MB).
-
Lecture 5. Summary and Practical Aspects. An Application
Example with the System Identification Toolbox of Matlab. Lecture Notes:
Lecture 5.
(Lesson Slides PDF Format, 6.6MB); (Lesson Slides PDF Zipped
Format, 4.6MB). (Lesson Slides PDF,
2 slides per page, 6.3MB).
-
Lecture 6. Course Summary on System Identification. Lecture Notes:
Lecture 6.
(Lesson Slides PDF Format, 2.8MB); (Lesson Slides PDF Zipped
Format, 2.2MB). (Lesson Slides PDF,
2 slides per page, 2.6MB).
-
Lecture 4.8. Closed Loop Identification. Theoretic Results and Practical Examples. Lecture Notes:
Lecture 4.8.
(Lesson Slides PDF Format, 1.2MB); (Lesson Slides PDF Zipped
Format, 1MB). (Lesson Slides PDF,
2 slides per page, 1MB).
Download
-
Simple example of Matlab function for the identification of a 2nd order ARX model: parameter and estimation
error computations (written by Silvio Simani, February 2007): (file in Matlab
format, 3KB).
-
Simple AR and ARX data files (Silvio Simani, February 2007): (text comment file for
the ARX example); (file in Matlab dat format).
(text comment file for the AR example); (file in Matlab dat format);
(file in Matlab format, generation example of the sun spots);
(data file in ascii .dat format, time series of the sun spots).
-
Matlab function "newhank.m" for construction of Hankel matrices (written by Silvio Simani, 2007):
(file in Matlab .m format).
-
Matlab function "struct_selectARX.m" for ARX model structure determination (written by Silvio Simani, 2007):
(file in Matlab .m format).
-
Matlab function "struct_selectARX_val2.m" for ARX model structure determination. Note that this function
uses two sets of identification and validation data (written by Silvio Simani, 2007):
(file in Matlab .m format).
-
Matlab functions for testing residual whiteness and Chi-square test (written by Silvio Simani, 2007):
(file in Matlab.m format).
-
Matlab functions for testing residual whiteness and Chi-square test. Note that this function
uses two sets of identification and validation data (written by Silvio Simani, 2007):
(file in Matlab.m format).
-
Data file from a simulated SISO (BJ) model (generated by Silvio Simani, 2007):
(file in ascii .dat format);
(Matlab .m file for BJ model data generation)
-
Data file from a simulated ARMA model (single output time series, generated by Silvio Simani, 2007):
(comment file in ascii .txt format);
(data file in ascii .dat format).
-
Process Data Sequences: DaISy (Database for the
Identification of Systems). Developed, maintained, and hosted by
SISTA .
-
General Process Data Sequences for ARX, AR, ARMAX, ARMA and ARIMAX model identification examples:
(Data text files in zipped format).
-
Process data sequences for the Buffer Vessel Example and few Matlab programme files:
(Data and Matlab function files in zipped format);
(Data file in .mat Matlab format, 49KB);
(Description file in .txt format).
-
Process data sequences for the energised R-phase 400 kV three phase transformer and the Matlab function
of the case study: (Data and Matlab function files in zipped format).
-
Process Data Sequences from SISTA website: (Dynamic Process Data Files in
zipped format downloaded from SISTA website).
-
K. Sigmon, "Matlab Primer Third Edition". University of Florida, Florida, Second
Edition ed., 1992. (file in formato pdf ).
-
SID, "System Identification Toolbox". System Identification Graphical User Interface for Matlab.
(file in formato pdf ).