Browsing by Author "Kao, YA"

Now showing 1 - 5 of 5

A new efficient LMS adaptive filtering algorithm
(1996-05-01)
Chen, SG
;
Kao, YA
;
Tsai, KY
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
A new efficient LMS adaptive filtering algorithm is proposed, The algorithm has comparable performance to that of the direct-form LMS algorithm (DLMS), while costs N/2 - 1 fewer multiplications at the expense of N/2 + 5 more additions than the DLMS algorithm, where N is the number of filter taps, The new algorithm has one more parameter adaptation than the DLMS algorithm. Further, the algorithm was combined with sign LMS algorithm (SA), signed regressor algorithm (SRA) and zero forcing (ZFA) algorithm for more complexity reduction. Simulation results showed that the new combined algorithms converge as fast as the direct-form SA, SRA and ZFA algorithms, meanwhile still maintain comparable performances.
Design of finite-word-length FIR filters with least-squares error
(2000-08-01)
Kao, YA
;
Chen, SG
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This paper proposes a new algorithm for designing finite word length linear-phase FIR filters. The new algorithm produces finite-precision least-squares error (LSE) solutions with much reduced search time than the brute-force full search algorithm. It is different from the full search algorithm that tries all possible combinations directly. The new algorithm utilizes geometric properties of a hyper-space to pinpoint potential solutions in a much more restricted way. Accordingly, a much smaller search space is generated. (C) 2000 Elsevier Science B.V. All rights reserved.
On the convergence and MSE of Chen's LMS adaptive algorithm
(1997)
Chen, SG
;
Kao, YA
;
Chen, CY
;
交大名義發表
;
電子工程學系及電子研究所
The recently proposed Chen's LMS algorithm [1] costs only half multiplications that of the conventional direct-form LMS algorithm (DLMS). Despite of the merit, the algorithm lacked rigorous theoretical analysis. This work intends to characterize its properties and conditions for mean and mean-square convergences. Closed-form MSE are derived, which is slightly larger than that of DLMS algorithm. It is shown, under the condition that the LMS step size mu is very small and an extra compensation step size alpha is properly chosen, Chen's algorithm has comparable performance to that of the DLMS algorithm. For the algorithm to converge, a tighter bound for alpha than before is also derived. The derived properties and conditions are verified by simulations.
On the properties of the reduction-by-composition LMS algorithm
(1999-11-01)
Chen, SG
;
Kao, YA
;
Chen, CY
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
The recently proposed low-complexity reduction-by-composition least-mean-square (LMS) algorithm (RCLMS) costs only half multiplications compared to that of the conventional direct-form LMS algorithm (DLMS), This work intends to characterize its properties and conditions for mean and mean-square convergence. Closed-form mean-square error (MSE) as a function of the LMS step-size mu and an extra compensation step-size alpha are derived, which are slightly larger than that of the DLMS algorithm. It is shown, when mu is small enough and alpha is properly chosen, the RCLMS algorithm has comparable performance to that of the DLMS algorithm. Simple working rules and ranges for alpha and mu to make such comparability are provided. For the algorithm to converge, a tight hound for alpha is also derived. The derived properties and conditions are verified by simulations.
The extrapolated LMS adaptive filtering algorithms
(1996)
Chen, SG
;
Kao, YA
;
Chen, CY
;
交大名義發表
;
電子工程學系及電子研究所
Two novel adaptive filtering algorithms are proposed in this work. The algorithms greatly reduce the number of adaptation steps of the LMS algorithm by extrapolating the adaptive filtering coefficients. The extrapolation is done through the time constant and optimal coefficient estimations of the coefficient adaptation process. Simulation results for the system identification and correlation canceller loop show significant reduction of the adaptation steps. The algorithms can be applied to general orthogonal adaptive filters and least-square error optimization problems.