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Lecture 3

Tid: Måndag 4 november 2013 kl 10:15 - 12:00 2013-11-04T10:15:00 2013-11-04T12:00:00

Kungliga Tekniska högskolan
FEL3201 Data Driven Modeling - Basic Course (8 credits)

Plats: V12

Info:

  • System Estimation Methods I: Basic principles and unstructured estimation

  • Non-parametric estimation
  • Least-squares estimation
  • The maximum likelihood method

Reading material

Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material

Lecturer

Håkan Hjalmarsson
Administratör Håkan Hjalmarsson skapade händelsen 11 oktober 2013
Administratör Håkan Hjalmarsson redigerade 11 oktober 2013

Models and Nonparametric  Methods Models of linear time invariant systems. Nonparametric time- and frequency-domain methods.

Reading material Ljung: Chapters 4, 6

Optional references Lemma 2.1

Ljung,1985

Administratör Håkan Hjalmarsson redigerade 11 oktober 2013

Models and Nonparametric  Methods Models of linear time invariant systems. Nonparametric time- and frequency-domain methods.

Reading material Ljung: Chapters 4, 6

Optional references Lemma 2.1

Ljung,1985

Lecturer Håkan Hjalmarsson¶

Administratör Håkan Hjalmarsson redigerade 11 oktober 2013

Models and Nonparametric  Methods Models of linear time invariSignal Estimation Methods Prediction, filtering antd systems. Nonparametric time- and frequency-domain methodsmoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapters 4, 63

Optional references Lemma 2.1

Ljung,1985¶ Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 11 oktober 2013

Signal Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapters 3

Optional references Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 11 oktober 2013

Signal Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filter.

Reading material Ljung: Chapter 3

Optional referencesmaterial Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson ändrade rättigheterna 14 oktober 2013

Kan därmed läsas av alla och ändras av håkan hjalmarsson (hjalmars@kth.se).
Administratör Håkan Hjalmarsson redigerade 15 oktober 2013

Signalystem Estimation Methods Prediction, filtering and smoothing. Optimal  filtering. Orthogonality condition. Wiener/Kalman/particle filterI: Basic principles and unstructured estimation
* .

 Reading material Ljung: Chapter 32.4, 6,

Optional material Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 16 oktober 2013

System Estimation Methods I: Basic principles and unstructured estimation
* .Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6,

Optional material Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 16 oktober 2013

System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not 208-211), 7.4

Optional material Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 16 oktober 2013

System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material Lemma 2.1

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 16 oktober 2013

System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* .The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material Lemma 2.1. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 16 oktober 2013


* System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
* L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.

 * Slides 1  
 Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 21 oktober 2013


* System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
* The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
* L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.
* Slides 1  
* Lemma 2.1
 Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 21 oktober 2013


* System Estimation Methods I: Basic principles and unstructured estimation
* Non-parametric estimation
* Least-squares estimation
* Prediction error minimization
 * The maximum likelihood method
Reading material Ljung: Chapter 2.4, 6, 7.1-7.3 (not pp. 208-211), 7.4

Optional material
* L. Ljung.     “On the Estimation of Transfer Functions”. Automatica, Vol. 21(6), pp. 677-696, 1985.
* Slides 1  
* Lemma 2.1
Lecturer Håkan Hjalmarsson

Administratör Håkan Hjalmarsson redigerade 23 oktober 2013

V12

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