Modern identification algorithm to reduce the complexity of parameter estimation using learning theory

Rustom Mamlook; Wiley E. Thompson

doi:10.1117/12.138247

9 July 1992 Modern identification algorithm to reduce the complexity of parameter estimation using learning theory

Rustom Mamlook, Wiley E. Thompson

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138247
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Rustom Mamlook and Wiley E. Thompson "Modern identification algorithm to reduce the complexity of parameter estimation using learning theory", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138247

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