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System Identification
broucher Course Brochure
System Identification
by Dr Arun K Tangirala

he field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction...

5.0  (262 Ratings) 781 Learner
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level English
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Program Fee
FREE
About This Course

he field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction...


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Course Curriculum
1 Sections | 118 Lecture | 53h 18min total length
Expand all sections
System Identification

Module

| 53h 18min total length
  • <?php echo $alt; ?>

    Lecture 01_Part 1 – Motivation and Overview 1

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    Lecture 01_Part 2 – Motivation and Overview 2

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    Lecture 02_Part 1 – Motivation and Overview 3

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    Lecture 02_Part 2 – Motivation and Overview 4

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    Lecture 03_Part 1 – Journey into Identification 1

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    Lecture 03_Part 2 – Journey into Identification 2

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    Lecture 03_Part 2 – Journey into Identification 2

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    Lecture 04_Part 1 – Journey into Identification 3

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    Lecture 04_Part 2 – Journey into Identification (Case Studies) 4

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    Lecture 04_Part 3 – Journey into Identification (Case Studies) 5

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    Lecture 05_Part 1 – Journey into Identification (Case Studies) 6

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    Lecture 05_Part 3 – Journey into Identification (Case Studies) 8

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    Lecture 06_Part 1 – Journey into Identification (Case Studies) 9

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    Lecture 06_Part 2 – Journey into Identification (Case Studies) 10

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    Lecture 07_Part 1 – Journey into Identification (Case Studies) 11

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    Lecture 07_Part 2 – Journey into Identification (Case Studies) 12

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    Lecture 07_Part 3 – Journey into Identification (Case Studies) 13

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    Lecture 08_Part 1 – Journey into Identification (Case Studies) 14

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    Lecture 08_Part 2 – Journey into Identification (Case Studies) 15

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    Lecture 09_Part 1 – Journey into Identification (Case Studies) 16

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    Lecture 09_Part 2 – Journey into Identification 17

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    Lecture 10_Part 1 – Journey into Identification 18

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    Lecture 10_Part 2 – Response-based Description 1

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    Lecture 10_Part 3 – Response-based Description 2

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    Lecture 11_Part 1 – Response-based Description 3

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    Lecture 11_Part 2 – Response-based Description 4

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    Lecture 12_Part 1 – Response-based Description 5

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    Lecture 12_Part 2 – Response-based Description 6

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    Lecture 12_Part 3 – Response-based Description 7

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    Lecture 13_Part 1 – Response-based Description 8

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    Lecture 13_Part 2 – Response-based Description 9

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    Lecture 14_Part 1 – Response-based Description 10

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    Lecture 14_Part 2 – Response-based Description 11

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    Lecture 14_Part 3 – Response-based Description 12

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    Lecture 15_Part 1 – Response-based Description 13

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    Lecture 15_Part 2 – Discrete time LTI system 1

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    Lecture 16_Part 1 – Discrete time LTI system 2

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    Lecture 16_Part 2 – z-Domain Descriptions 1

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    Lecture 17_Part 1 – z-Domain Descriptions 2

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    Lecture 17_Part 2 – z-Domain Descriptions 3

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    Lecture 18_Part 1 – z-Domain Descriptions 4

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    Lecture 18_Part 2 – z-Domain Descriptions 5

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    Lecture 18_Part 3 – z-Domain Descriptions 6

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    Lecture 19_Part 1 – State Space Representation 1

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    Lecture 19_Part 2 – State Space Representation 2

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    Lecture 20_Part 1 – State Space Representation 3

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    Lecture 20_Part 2 – State Space Representation 4

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    Lecture 21_Part 1 – Sampled - Data Systems 1

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    Lecture 21_Part 2 – Sampled - Data Systems 2

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    Lecture 22_Part 1 – Sampled - Data Systems 3

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    Lecture 22_Part 2 – Sampled - Data Systems 4

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    Lecture 23_Part 1 – Sampled - Data Systems 5

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    Lecture 23_Part 2 – Sampled - Data Systems 6

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    Lecture 24_Part 1 – Sampled - Data Systems 7

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    Lecture 24_Part 2 – Sampled - Data Systems 8

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    Lecture 25_Part 1 – Probability_Random variables and moments - Review 1

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    Lecture 25_Part 2 – Probability_Random variables and moments - Review 2

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    Lecture 26_Part 1 – Probability_Random variables and moments - Review 3

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    Lecture 26_Part 2 – Probability_Random variables and moments - Review 4

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    Lecture 27_Part 1 – Probability_Random variables and moments - Review 5

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    Lecture 27_Part 2 – Probability_Random variables and moments - Review 6

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    Lecture 28_Part 1 – Random Processes - Review 1

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    Lecture 28_Part 2 – Random Processes - Review 2

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    Lecture 29_Part 1 – Random Processes - Review 3

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    Lecture 29_Part 2 – Random Processes - Review 4

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    Lecture 30_Part 1 – Random Processes - Review 5

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    Lecture 30_Part 2 – Random Processes - Review – (MATLAB) 6

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    Lecture 31_Part 1 – Random Processes - Review 7

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    Lecture 31_Part 2 – Random Processes - Review 8

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    Lecture 32_Part 1 – Spectral Representation 1

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    Lecture 32_Part 2A – Spectral Representation 2

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    Lecture 32_Part 2B – Spectral Representation 3

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    Lecture 33_Part 1 – Models for Identification 1

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    Lecture 33_Part 2 – Models for Identification 2

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    Lecture 34_Part 1 – Models for Identification 3

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    Lecture 34_Part 2 – Models for Identification 4

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    Lecture 35_Part 1 – One step and multi-step ahead prediction 1

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    Lecture 35_Part 2 – One step and multi-step ahead prediction 2

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    Lecture 36_Part 1 – One step and multi-step ahead prediction 3

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    Lecture 36_Part 2 – One step and multi-step ahead prediction 4

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    Lecture 36_Part 3 – One step and multi-step ahead prediction 5

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    Lecture 37_Part 1 – Introduction to estimation theory 1

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    Lecture 37_Part 2 – Introduction to estimation theory 2

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    Lecture 38_Part 1 – Fisher’s information and properties of estimators 1

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    Lecture 38_Part 2 – Fisher’s information and properties of estimators 2

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    Lecture 39_Part 1 – Fisher’s information and properties of estimators 3

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    Lecture 39_Part 2 – Fisher’s information and properties of estimators 4

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    Lecture 40_Part 1 – Fisher’s information and properties of estimators 5

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    Lecture 40_Part 2 – Fisher’s information and properties of estimators 6

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    Lecture 41_Part 1 – Fisher’s information and properties of estimators 7

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    Lecture 41_Part 2 – Fisher’s information and properties of estimators 8

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    Lecture 42_Part 1 – Fisher’s information and properties of estimators 9

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    Lecture 43_Part 1 – Fisher’s information and properties of estimators 11

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    Lecture 43_Part 2 – Fisher’s information and properties of estimators 12

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    Lecture 43_Part 3 – Fisher’s information and properties of estimators 13

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    Lecture 43_Part 4 – Fisher’s information and properties of estimators 14

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    Lecture 44_Part 1 – Fisher’s information and properties of estimators 15

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    Lecture 45_Part 1 – Estimation of non-parametric model 1

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    Lecture 45_Part 2 – Estimation of non-parametric model 2

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    Lecture 46_Part 1 – Estimation of non-parametric model 3

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    Lecture 46_Part 2 – Estimation of non-parametric model 4

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    Lecture 47_Part 2 – Estimation of non-parametric model 4

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    Lecture 47_Part 3 – Estimation of non-parametric model 5

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    Lecture 48_Part 1 – Estimation of parametric model 1

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    Lecture 48_Part 2 – Estimation of parametric model 2

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    Lecture 48_Part 3 – Estimation of parametric model 3

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    Lecture 49_Part 1 – Estimation of parametric model 4

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    Lecture 49_Part 2 – State-Space/Subspace identification 1

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    Lecture 50_Part 1 – State-Space/Subspace identification 2

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    Lecture 50_Part 2 – State-Space/Subspace identification 3

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    Lecture 51_Part 1 – State-Space/Subspace identification 4

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    Lecture 51_Part 2 – State-Space/Subspace identification 5

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    Lecture 51_Part 3 – State-Space/Subspace identification 6

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    Lecture 51_Part 4 – State-Space/Subspace identification 7

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    Lecture 51_Part 5 – State-Space/Subspace identification 8

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    Lecture 52_Part 1 – Input for Identification

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    Lecture 52_Part 2 – Input for Identification

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    Lecture 52_Part 3 – Input for Identification

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Review & Ratings

5.0
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