Distributed Computation: Consensus Algorithms in Networked Control Systems

Related lectures (29)

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Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Introduces Hadamard and Plackett-Burman designs, explaining construction, analysis, and optimization techniques.

Explains the method of least squares and normal equations for finding optimal solutions.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Delves into the impact of quantization on networked control systems, exploring stability, performance, and design trade-offs.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Explores the Laplacian matrix, time-varying consensus theorems, and balanced graphs in networked control systems.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.