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Get Algorithms and parallel VLSI architectures III: proceedings PDF

By M. Moonen, F. Catthoor

ISBN-10: 0444821066

ISBN-13: 9780444821065

A finished assessment of the present evolution of analysis in algorithms, architectures and compilation for parallel structures is equipped by way of this publication.

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Read Online or Download Algorithms and parallel VLSI architectures III: proceedings of the International Workshop, Algorithms and Parallel VLSI Architectures III, Leuven, Belgium, August 29-31, 1994 PDF

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Additional resources for Algorithms and parallel VLSI architectures III: proceedings of the International Workshop, Algorithms and Parallel VLSI Architectures III, Leuven, Belgium, August 29-31, 1994

Example text

KEYWOttDS. Systolic array, normalization, fixed point, probability. 1 INTRODUCTION A normalized version of the QR algorithm [3], [7] for recursive least squares estimation and filtering is presented. All data and parameters of the main triangular section of the array have the guaranteed range of values [-1,1]. The array has the potential for a minimallatency-implementatlon, because the normalized section can use DSP, or VLSI, fixed point hardware (look-up-tables) [2]. An understanding of the numerical properties of the normalized ttLS algorithm is attempted using a global probability numerical analysis.

The oscillations of Rll for zero input are due to the accumulated data updates. 5 CONCLUSIONS In this paper, we have implemented the block-regularized exponential forgetting in the square-root RLS algorithm with inverse updates [12]. The principle of regularization consists of weighting the RLS cost function (2) with an alternative function, specified by the user. This weighting prevents the parameters of the cost function from numerical instability in the case of non-informative data, because they converge to the regularizing values in this case.

McWhirter and L K. Proudler related to the Kalman gain vector). Secondly, the orthogonal matrix 0y(n) can be generated from knowledge of the matrix RyT(n - 1) and the new data vector. Specifically, t~y(n) is the orthogonal matrix given by: = (8) n where ~_y(n) = RyT(n-1)F~p(n)l (9) b, (n)J This can easily be proved as follows. Let Oy(n) L[~,(n), y (n) = U (10) From equation (9) it follows that [o] (11) and hence ~_ = 0. If 0(n) is constructed as a sequence of Givens rotations which preserves the structure of the upper triangular matrix in equation (10), it follows that U = Ry(n).

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Algorithms and parallel VLSI architectures III: proceedings of the International Workshop, Algorithms and Parallel VLSI Architectures III, Leuven, Belgium, August 29-31, 1994 by M. Moonen, F. Catthoor


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