Abstract
So far, we have considered the least squares solution to a particularly simple es- 3 timation problem in a single unknown parameter. A more general problem is the estimation of the n unknown parameters aj , j = 1, 2, . . . ,n, appearing in a general nth order linear regression relationship of the form, $$ x(k)={a_1}{x_1}(k)+{a_2}{x_2}(k) +\cdots +{a_n}{x_n}(k)$$
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CITATION STYLE
APA
Lewis, J. M., Lakshmivarahan, S., & Dhall, S. (2009). Recursive least squares estimation. In Dynamic Data Assimilation (pp. 141–146). Cambridge University Press. https://doi.org/10.1017/cbo9780511526480.009
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