Lecture 1 | Introduction to Linear Dynamical Systems
1:16:46Lecture 2 | Introduction to Linear Dynamical Systems
1:05:52Lecture 3 | Introduction to Linear Dynamical Systems
1:19:10Lecture 4 | Introduction to Linear Dynamical Systems
1:14:08Lecture 5 | Introduction to Linear Dynamical Systems
1:15:14Lecture 6 | Introduction to Linear Dynamical Systems
1:16:19Lecture 7 | Introduction to Linear Dynamical Systems
1:15:46Lecture 8: Least-Norm Solutions of Underdetermined Equations (EE263)
1:15:58Lecture 9: Autonomous Linear Dynamical Systems (EE263)
1:09:02Lecture 10: Autonomous Linear Dynamical Systems in Electrical Engineering (EE263)
1:11:42Lecture 11: Solutions via Laplace Transform and Matrix Exponentials (EE263)
1:08:55Lecture 12: Matrix Exponentials, Eigenvectors, and Diagonalization in LDS (EE263)
1:13:37Lecture 13: Generalized Eigenvectors, Diagonalization, and Jordan Canonical Form (EE263)
1:13:02Lecture 14: Applications of Jordan Canonical Form in LDS and Electrical Engineering (EE263)
1:17:43Lecture 15: Inputs, Outputs, and Symmetric Matrices in Linear Dynamical Systems (EE263)
1:09:01Lecture 16: Symmetric Matrices, Quadratic Forms, Matrix Norm, and SVDs in LDS (EE263)
1:12:35Lecture 17: Applications of Singular Value Decomposition in LDS and Electrical Engineering (EE263)
1:16:52Lecture 18: Applications of SVD, Controllability, and State Transfer in Electrical Engineering (EE263)
1:15:14Lecture 19: Controllability and State Transfer in Modern Electrical Engineering (EE263)
1:10:30Lecture 20: Observability and State Estimation (EE263)
1:09:25Lecture 12: Matrix Exponentials, Eigenvectors, and Diagonalization in LDS (EE263)
The lecture elaborates on the interplay between matrix exponentials, eigenvectors, and diagonalization in the context of linear dynamical systems. It demonstrates how these concepts simplify system analysis.
Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on matrix exponentials, eigenvectors, and diagonalization and their uses in LDS 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.
Lecture 11: Solutions via Laplace Transform and Matrix Exponentials (EE263)
Next · Lecture 13Lecture 13: Generalized Eigenvectors, Diagonalization, and Jordan Canonical Form (EE263)