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Abstract: According to the hierarchical identification principle, a hierarchical gradient based iterative estimation algorithm is derived for multivariable output error moving average systems (i.e., multivariable OEMA-like models) which is different from multivariable CARMA-like models. As there exist unmeasurable noise-free outputs and unknown noise terms in the information vector/matrix of the corresponding identification model, this paper is, by means of the auxiliary model identification idea, to replace the unmeasurable variables in the information vector/matrix with the estimated residuals and the outputs of the auxiliary model. A numerical example is provided. [ABSTRACT FROM AUTHOR]

Abstract: An algorithmic characterization of H-matrices was provided by Huang et al. [Comput. Math. Appl. 48 (2004) 1587–1601]. In this paper, we propose a new non-parameter method, which is always convergent in finite iterative steps for H-matrices and needs fewer number of iterations than that of Huang et al.; we also provide an improved algorithm for a general matrix, which decreases the wasteful computations when the given matrix is not an H-matrix. Several numerical examples for the effectiveness of the proposed algorithms are presented. [Copyright &y& Elsevier]