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On asymptotic properties of real-time identification algorithms based on dynamic stochastic approximation

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1 Author(s)
Stankovic, S. ; University of Belgrade, Belgrade, Yugoslavia

The algorithms of dynamic stochastic approximation type are proposed for real-time identification of multivariable linear dynamic discrete-time systems with stochastic parameters. These algorithms can be considered as the general representatives of a class of gradient-type equation-error recursive identification methods. The analysis of their asymptotic properties is presented. It is proved that the algorithms converge either in the mean-square sense, or in the sense of keeping the mean-square error bounded, depending on system parameter properties. Convergence conditions are expressed in terms of inherent system characteristics, e.g., properties of the impulse response matrices and their realizations. A large class of input random processes is supposed.

Published in:

Automatic Control, IEEE Transactions on  (Volume:23 ,  Issue: 1 )

Date of Publication:

Feb 1978

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