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Continuous-time recursive least-squares estimation, adaptive neural networks and systolic arrays

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3 Author(s)
J. Dehaene ; ESAT-SISTA Lab., Katholieke Univ., Leuven, Belgium ; M. Moonen ; J. Vandewalle

We derive square-root covariance-type and information-type algorithms for continuous-time recursive least-squares estimation. The algorithms allow for easy manipulation and uniform parallelization. They are related to well-known neural adaptation laws and can be considered as continuous-time limits of systolic arrays

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IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:42 ,  Issue: 2 )