Course103106149120 lectures
System Identification
Dr. Arun K.Tangirala · IIT Madras
Lectures
Module 01
- Lecture 01_Part 1 - Motivation and Overview 1
- Lecture 01_Part 2 - Motivation and Overview 2
- Lecture 02_Part 1 - Motivation and Overview 3
- Lecture 02_Part 2 - Motivation and Overview 4
- Lecture 03_Part 1 - Journey into Identification 1
- Lecture 03_Part 2 - Journey into Identification 2
- Lecture 04_Part 1 - Journey into Identification 3
- Lecture 04_Part 2 - Journey into Identification (Case Studies) 4
- Lecture 04_Part 3 - Journey into Identification (Case Studies) 5
- Lecture 05_Part 1 - Journey into Identification (Case Studies) 6
- Lecture 05_Part 2 - Journey into Identification (Case Studies) 7
- Lecture 05_Part 3 - Journey into Identification (Case Studies) 8
- Lecture 06_Part 1 - Journey into Identification (Case Studies) 9
- Lecture 06_Part 2 - Journey into Identification (Case Studies) 10
- Lecture 07_Part 1 - Journey into Identification (Case Studies) 11
- Lecture 07_Part 2 - Journey into Identification (Case Studies) 12
- Lecture 07_Part 3 - Journey into Identification (Case Studies) 13
- Lecture 08_Part 1 - Journey into Identification (Case Studies) 14
- Lecture 08_Part 2 - Journey into Identification (Case Studies) 15
- Lecture 09_Part 1 - Journey into Identification (Case Studies) 16
- Lecture 09_Part 2 - Journey into Identification 17
- Lecture 10_Part 1 - Journey into Identification 18
- Lecture 10_Part 2 - Response-based Description 1
- Lecture 10_Part 3 - Response-based Description 2
- Lecture 11_Part 1 - Response-based Description 3
- Lecture 11_Part 2 - Response-based Description 4
- Lecture 12_Part 1 - Response-based Description 5
- Lecture 12_Part 2 - Response-based Description 6
- Lecture 12_Part 3 - Response-based Description 7
- Lecture 13_Part 1 - Response-based Description 8
- Lecture 13_Part 2 - Response-based Description 9
- Lecture 14_Part 1 - Response-based Description 10
- Lecture 14_Part 2 - Response-based Description 11
- Lecture 14_Part 3 - Response-based Description 12
- Lecture 15_Part 1 - Response-based Description 13
- Lecture 15_Part 2 - Discrete time LTI system 1
- Lecture 16_Part 1 - Discrete time LTI system 2
- Lecture 16_Part 2 - z-Domain Descriptions 1
- Lecture 17_Part 1 - z-Domain Descriptions 2
- Lecture 17_Part 2 - z-Domain Descriptions 3
- Lecture 18_Part 1 - z-Domain Descriptions 4
- Lecture 18_Part 2 - z-Domain Descriptions 5
- Lecture 18_Part 3 - z-Domain Descriptions 6
- Lecture 19_Part 1 - State Space Representation 1
- Lecture 19_Part 2 - State Space Representation 2
- Lecture 20_Part 1 - State Space Representation 3
- Lecture 20_Part 2 - State Space Representation 4
- Lecture 21_Part 1 - Sampled - Data Systems 1
- Lecture 21_Part 2 - Sampled - Data Systems 2
- Lecture 22_Part 1 - Sampled - Data Systems 3
- Lecture 22_Part 2 - Sampled - Data Systems 4
- Lecture 23_Part 1 - Sampled - Data Systems 5
- Lecture 23_Part 2 - Sampled - Data Systems 6
- Lecture 24_Part 1 - Sampled - Data Systems 7
- Lecture 24_Part 2 - Sampled - Data Systems 8
- Lecture 25_Part 1 - Probability_Random variables and moments - Review 1
- Lecture 25_Part 2 - Probability_Random variables and moments - Review 2
- Lecture 26_Part 1 - Probability_Random variables and moments - Review 3
- Lecture 26_Part 2 - Probability_Random variables and moments - Review 4
- Lecture 27_Part 1 - Probability_Random variables and moments - Review 5
- Lecture 27_Part 2 - Probability_Random variables and moments - Review 6
- Lecture 28_Part 1 - Random Processes - Review 1
- Lecture 28_Part 2 - Random Processes - Review 2
- Lecture 29_Part 1 - Random Processes - Review 3
- Lecture 29_Part 2 - Random Processes - Review 4
- Lecture 30_Part 1 - Random Processes - Review 5
- Lecture 30_Part 2 - Random Processes - Review - (MATLAB) 6
- Lecture 31_Part 1 - Random Processes - Review 7
- Lecture 31_Part 2 - Random Processes - Review 8
- Lecture 32_Part 1 - Spectral Representation 1
- Lecture 32_Part 2A - Spectral Representation 2
- Lecture 32_Part 2B - Spectral Representation 3
- Lecture 33_Part 1 - Models for Identification 1
- Lecture 33_Part 2 - Models for Identification 2
- Lecture 34_Part 1 - Models for Identification 3
- Lecture 34_Part 2 - Models for Identification 4
- Lecture 35_Part 1 - One step and multi-step ahead prediction 1
- Lecture 35_Part 2 - One step and multi-step ahead prediction 2
- Lecture 36_Part 1 - One step and multi-step ahead prediction 3
- Lecture 36_Part 2 - One step and multi-step ahead prediction 4
- Lecture 36_Part 3 - One step and multi-step ahead prediction 5
- Lecture 37_Part 1 - Introduction to estimation theory 1
- Lecture 37_Part 2 - Introduction to estimation theory 2
- Lecture 38_Part 1 - Fisher's information and properties of estimators 1
- Lecture 38_Part 2 - Fisher's information and properties of estimators 2
- Lecture 39_Part 1 - Fisher's information and properties of estimators 3
- Lecture 39_Part 2 - Fisher's information and properties of estimators 4
- Lecture 40_Part 1 - Fisher's information and properties of estimators 5
- Lecture 40_Part 2 - Fisher's information and properties of estimators 6
- Lecture 41_Part 1 - Fisher's information and properties of estimators 7
- Lecture 41_Part 2 - Fisher's information and properties of estimators 8
- Lecture 42_Part 1 - Fisher's information and properties of estimators 9
- Lecture 42_Part 2 - Fisher's information and properties of estimators 10
- Lecture 43_Part 1 - Fisher's information and properties of estimators 11
- Lecture 43_Part 2 - Fisher's information and properties of estimators 12
- Lecture 43_Part 3 - Fisher's information and properties of estimators 13
- Lecture 43_Part 4 - Fisher's information and properties of estimators 14
- Lecture 44_Part 1 - Fisher's information and properties of estimators 15
- Lecture 45_Part 1 - Estimation of non-parametric model 1
- Lecture 45_Part 2 - Estimation of non-parametric model 2
- Lecture 47_Part 1 - Estimation of non-parametric model 3
- Lecture 47_Part 2 - Estimation of non-parametric model 4
- Lecture 47_Part 3 - Estimation of non-parametric model 5
- Lecture 46_Part 1 - Estimation of non-parametric model 3
- Lecture 46_Part 2 - Estimation of non-parametric model 4
- Lecture 48_Part 1 - Estimation of parametric model 1
- Lecture 48_Part 2 - Estimation of parametric model 2
- Lecture 48_Part 3 - Estimation of parametric model 3
- Lecture 49_Part 1 - Estimation of parametric model 4
- Lecture 49_Part 2 - State-Space/Subspace identification 1
- Lecture 50_Part 1 - State-Space/Subspace identification 2
- Lecture 50_Part 2 - State-Space/Subspace identification 3
- Lecture 51_Part 1 - State-Space/Subspace identification 4
- Lecture 51_Part 2 - State-Space/Subspace identification 5
- Lecture 51_Part 3 - State-Space/Subspace identification 6
- Lecture 51_Part 4 - State-Space/Subspace identification 7
- Lecture 51_Part 5 - State-Space/Subspace identification 8
- Lecture 52_Part 1 - Input for Identification
- Lecture 52_Part 2 - Input for Identification
- Lecture 52_Part 3 - Input for Identification
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Madras
- Department
- Chemical Engineering
- Teacher
- Dr. Arun K.Tangirala
- Level
- Undergraduate
- Language
- English
- Subjects
- Chemistry, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed
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