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Lecture
Networked Control Systems: Properties and Connectivity
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Related lectures (30)
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Graphical Models: Representing Probabilistic Distributions
Covers graphical models for probabilistic distributions using graphs, nodes, and edges.
Networked Control Systems: Graph Theory and Stochastic Matrices
Explores graph theory, stochastic matrices, consensus algorithms, and spectral properties in networked control systems.
Networked Control Systems: Opportunities
Explores coordination in networked control systems, graph theory, and consensus algorithms.
Irreducible Matrices and Strong Connectivity
Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.
Matrices and Networks
Explores the application of matrices and eigendecompositions in networks.
Expander Graphs: Properties and Eigenvalues
Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.
Networked Control Systems: Laplacian Matrix and Consensus
Explores the Laplacian matrix and consensus in networked control systems.
Laplacian Matrix: Properties and Examples
Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.
Graphs: BFS
Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.
Graphical Models: Probability Distributions and Factor Graphs
Covers graphical models for probability distributions and factor graphs representation.
