Balanced Realization: SISO Case

Related lectures (26)

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Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Explores spectral and singular value decompositions of matrices.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores the diagonalization of symmetric matrices and the importance of Singular Value Decomposition.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Delves into singular value decomposition and its applications in linear algebra.

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.