The theory of "continuous learning" is applied here to the design of nonlinear sampled-data trackers. This theory provides a continuous-motion approximation of the discrete or sampled motion of the actual tracker. The theory prodicts the transient-response performance of the tracker as well as the mean-square errors caused by noise and statistical fluctuations in the signal. Numerical examples of first-order and second-order trackers designed by this technique are presented. These examples illustrate the adaptive behavior predicted by the technique. In one of these examples the trade-off between transient-response performance and the suppression of noise-induced tracking errors is demonstrated.


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    Title :

    Adaptive Trackers Based on Continuous Learning Theory


    Contributors:
    Bershad, N.J. (author) / Merryman, P. (author) / Sklansky, J. (author)

    Published in:

    Publication date :

    1974-03-01


    Size :

    2471512 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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