The computational efficiency of an algebraically equivalent alternative formulation of the eigensystem realization algorithm is investigated. This alternative can process large data sets much faster than the form of the eigensystem realization algorithm that is ordinarily used. This is primarily due to the partial eigenvalue decomposition of the inner or outer product of the Hankel matrix and its transpose, whichever is smaller, in place of the full singular value decomposition of the Hankel matrix itself or of the Hankel matrix square. By computing only the largest subset of the Hankel matrix product eigenvalues, low-order models tan be realized from very large data sets. The primary benefit of this is improved convergence on the model pole locations for practical modal identification problems using hundreds of sensors to identify hundreds of modal resonances. This paper investigates the computational speed increase over the original algorithm on experimental data and assesses the magnitude of model errors introduced by squaring the Hankel matrix. In addition, key computational strategies are presented for efficient implementation of the algorithm on modern computer architectures. References: 16
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