Electrical Engineering 263: Linear Dynamical Systems
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Electrical Engineering 263: Linear Dynamical Systems

Introduction to Linear Dynamical Systems (EE263) is the introduction to applied linear algebra and linear dynamical syst
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
187 students enrolled
Published 3/2010
English
Electrical Engineering 263: Linear Dynamical Systems
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Description
Introduction to Linear Dynamical Systems (EE263) is the introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares approximations 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 and transfer matrix descriptions. Control, reachability, state transfer, and least-norm inputs. Observability and least-squares state estimation. EE263 covers some of the same topics, but is complementary to, CME200.
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Curriculum For This Course
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Introduction to Linear Dynamical Systems
20 Lectures 00:00
Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, gives an overview of 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on linear functions 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, gives a review of linear algebra 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on orthonormal sets of vectors and QR factorization 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on QR factorization and least squares 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the applications of least squares 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on regularized least squares and the Gauss-Newton method 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the least norm solutions of undetermined equations 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on autonomous linear dynamical systems 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 approximations 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 and transfer matrix descriptions.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on autonomous linear dynamical systems and how they relate to Electrical Engineering 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on how to find solutions via LaPlace transform and the use of matrix exponentials 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on generalized eigenvectors, diagonalization, and Jordan canonical form 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the applications of Jordan canonical form in LDS and electrical engineering 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on inputs and outputs of linear dynamical systems, as well as symmetric matrices 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the use of symmetric matrices, quadratic forms, matrix norm, and SVDs 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the use of symmetric matrices, quadratic forms, matrix norm, and SVDs 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the applications of SVD, controllability, and state transfer in electrical engineering 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on controllability and state transfer and their uses in modern electrical engineering 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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent

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 approximations 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.
Introduction to Linear Dynamical Systems
ImportContent
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Stanford University
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