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References in periodicals archive ?
Stabilization of total least squares problems by introducing a quadratic constraint was extensively studied in [2, 7, 12, 14, 15, 16, 17, 19, 24, 26, 27, 28].
Dual regularized total least squares. In Section 2.1, important basic properties of the dual RTLS problem are summarized and connections to related problems are regarded, especially the connection to the RTLS problem (1.3).
Algorithm 1 Dual Regularized Total Least Squares Basis Method.
Algorithm 3 Dual Regularized Total Least Squares Method.
The adaptive total least squares problem is a minimization problem of the form [10,11]:
Least squares misalignment [parallel][w.sub.n] - [w.sub.opt][parallel] is compared with the total least squares misalignment [parallel][w.sub.n] - [w.sub.opt][parallel]/[square root of [w.sup.T.sub.n][[??].sub.n]], and simulations results are recorded for 2000 iterations with an ensemble average of 1000 independent runs.
The first two compute a least squares solution of adaptive total least squares problem, while the third one computes TLS solution of adaptive total least squares problem.
In this paper, an efficient TLMS algorithm is presented for the total least squares solution of adaptive filtering problem.
Table 1 shows the results of the Total Least squares estimation for the population data described in Previous discussions of PVA.
Consequently the Total Least squares estimation produces a negative carrying capacity.
In the absence of catastrophes, the Total Least squares estimate for a probability of extinction within 100 yr starting from a population of 11 is 3.4 x [10.sup.-8], with a confidence interval of (9.7 x [10.sup.-11], 1.2 x [10.sup.-5]).
The method of Total Least squares appears to be adequate to deal with the bias when sufficient information about the observation errors is available, but of course it cannot compensate for lack of information.
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