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Stanford EE263: Introduction to Linear Dynamical Systems →0/20
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Lecture 1 | Introduction to Linear Dynamical Systems

1:16:46
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Lecture 2 | Introduction to Linear Dynamical Systems

1:05:52
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Lecture 3 | Introduction to Linear Dynamical Systems

1:19:10
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Lecture 4 | Introduction to Linear Dynamical Systems

1:14:08
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Lecture 5 | Introduction to Linear Dynamical Systems

1:15:14
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Lecture 6 | Introduction to Linear Dynamical Systems

1:16:19
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Lecture 7 | Introduction to Linear Dynamical Systems

1:15:46
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Lecture 8: Least-Norm Solutions of Underdetermined Equations (EE263)

1:15:58
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Lecture 9: Autonomous Linear Dynamical Systems (EE263)

1:09:02
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Lecture 10: Autonomous Linear Dynamical Systems in Electrical Engineering (EE263)

1:11:42
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Lecture 11: Solutions via Laplace Transform and Matrix Exponentials (EE263)

1:08:55
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Lecture 12: Matrix Exponentials, Eigenvectors, and Diagonalization in LDS (EE263)

1:13:37
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Lecture 13: Generalized Eigenvectors, Diagonalization, and Jordan Canonical Form (EE263)

1:13:02
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Lecture 14: Applications of Jordan Canonical Form in LDS and Electrical Engineering (EE263)

1:17:43
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Lecture 15: Inputs, Outputs, and Symmetric Matrices in Linear Dynamical Systems (EE263)

1:09:01
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Lecture 16: Symmetric Matrices, Quadratic Forms, Matrix Norm, and SVDs in LDS (EE263)

1:12:35
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Lecture 17: Applications of Singular Value Decomposition in LDS and Electrical Engineering (EE263)

1:16:52
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Lecture 18: Applications of SVD, Controllability, and State Transfer in Electrical Engineering (EE263)

1:15:14
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Lecture 19: Controllability and State Transfer in Modern Electrical Engineering (EE263)

1:10:30
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Lecture 20: Observability and State Estimation (EE263)

1:09:25

Lecture 20: Observability and State Estimation (EE263)

Stephen BoydStanford University

Professor

1:09:25·2008·graduate level

This final lecture covers the essential concepts of observability and state estimation in linear dynamical systems, providing methods to infer internal system states from external measurements. It concludes the core topics.

MathematicsEngineeringLinear AlgebraSignal Processing & RoboticsControl & Dynamical SystemsObservability

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, gives his final lecture on observability and state estimation for the course, Introduction to Linear Dynamical Systems (EE263).

Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input multi-output systems, impulse and step matrices; convolution.

Previous · Lecture 19

Lecture 19: Controllability and State Transfer in Modern Electrical Engineering (EE263)

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