H. Yao, M. Ikenoue, S. Kanae, Z.-J. Yang, and K. Wada (Japan)
system identification, parameter estimation, total least squares, structured total least squares, dynamic total least squares
The Structured Total Least Squares (STLS) problem is an extension of the Total Least Squares (TLS) problem for solving an overdetermined system of equations Ax ≈ b1) . The Dynamic Total Least Squares (DTLS) problem is a special case of STLS problem. In this paper, we illus trate the TLS singular value problem by defining some vec tors from a new viewpoint. And we also derive the DTLS nonlinear singular value problem by this new viewpoint. Futhermore, we introduce a heuristic Iterative Quadratic Maximum Likelihood (IQML)-like algorithm for solving DTLS nonlinear problem. The main objective of this paper is to compare the difference between this variety of IQML like algorithm and the Inverse Iteration algorithm for solv ing the DTLS nonlinear singular value problem by simu lation experiments with some conditions. The simulation results show that the convergence rate of IQML-like algo rithm is faster than that of Inverse Iteration algorithm under the same conditions and the computational efficiency of the IQML-like algorithm is also higher compared with the In verse Iteration algorithm.
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