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Proceedings Paper

Modern identification algorithm to reduce the complexity of parameter estimation using learning theory
Author(s): Rustom Mamlook; Wiley E. Thompson
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Paper Abstract

A modern identification algorithm to reduce the complexity of estimating parameters for discrete time-invariant linear systems and nonlinear systems is presented. The algorithm requires no a priori knowledge of the input or of the order of the system. An identification unbiased estimator method is presented which reduces the computational complexity of covariance matrix inversion. Probability one convergence of the estimated parameters to their true values is presented, and stability of the identification algorithm is discussed. An example is presented to illustrate the results.

Paper Details

Date Published: 9 July 1992
PDF: 10 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138247
Show Author Affiliations
Rustom Mamlook, New Mexico State Univ. (United States)
Wiley E. Thompson, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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