A technique for the identification of linear systems Export

@article{citeulike:3708995, abstract = {An iterative technique is proposed to identify a linear system from samples of its input and output in the presence of noise by minimizing the mean-square error between system and model outputs. The model chosen has a transfer function which is a ratio of polynomials in z^-1. Although the regression equations for the optimal set of coefficients are highly nonlinear and intractable, it is shown that the problem can be reduced to the repeated solution of a related linear problem. Computer simulation of a number of typical discrete systems is used to demonstrate the considerable improvement over the Kalman estimate which can be obtained in a few iterations. The procedure is found to be effective at signal-to-noise ratios less than unity, and with as few as 200 samples of the input and output records.}, author = {Steiglitz, K. and Mcbride, L.}, booktitle = {Automatic Control, IEEE Transactions on}, citeulike-article-id = {3708995}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1098181}, journal = {Automatic Control, IEEE Transactions on}, number = {4}, pages = {461--464}, posted-at = {2008-11-26 19:21:02}, priority = {5}, title = {A technique for the identification of linear systems}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1098181}, volume = {10}, year = {1965} }

TY - JOUR ID - citeulike:3708995 L3 - citeulike-article-id:3708995 N2 - An iterative technique is proposed to identify a linear system from samples of its input and output in the presence of noise by minimizing the mean-square error between system and model outputs. The model chosen has a transfer function which is a ratio of polynomials in z^-1. Although the regression equations for the optimal set of coefficients are highly nonlinear and intractable, it is shown that the problem can be reduced to the repeated solution of a related linear problem. Computer simulation of a number of typical discrete systems is used to demonstrate the considerable improvement over the Kalman estimate which can be obtained in a few iterations. The procedure is found to be effective at signal-to-noise ratios less than unity, and with as few as 200 samples of the input and output records. IS - 4 JF - Automatic Control, IEEE Transactions on EP - 464 TI - A technique for the identification of linear systems VL - 10 T2 - Automatic Control, IEEE Transactions on SP - 461 AU - Steiglitz, K AU - Mcbride, L PY - 1965/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1098181 ER -