On almost-sure bounds for the LMS algorithm
Almost-sure (a.s.) bounds for linear, constant-gain, adaptive filtering algorithms are investigated. For instance, under general pseudo-stationarity and dependence conditions on the driving data {/spl psi/k,k=1,2,3,...}, {Y/sub k/,k=0,1,2,...} a.s. convergence and rates of a.s. convergence (as the a...
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| Published in | IEEE transactions on information theory Vol. 40; no. 2; pp. 372 - 383 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.03.1994
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9448 1557-9654 |
| DOI | 10.1109/18.312160 |
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| Abstract | Almost-sure (a.s.) bounds for linear, constant-gain, adaptive filtering algorithms are investigated. For instance, under general pseudo-stationarity and dependence conditions on the driving data {/spl psi/k,k=1,2,3,...}, {Y/sub k/,k=0,1,2,...} a.s. convergence and rates of a.s. convergence (as the algorithm gain /spl epsiv//spl rarr/0) are established for the LMS algorithm h/sub k+1//sup /spl epsiv//=h/sub k//sup /spl epsiv//+/spl epsiv/Y/sub k/(/spl psi//sub k+1/-Y/sub k//sup T/h/sub k//sup /spl epsiv//) subject to some nonrandom initial condition h/sub 0//sup /spl epsiv//=h/sub 0/. In particular, defining {g/sub k//sup /spl epsiv//}/sub k=0//sup /spl infin// by g/sub 0//sup /spl epsiv//=h/sub 0/ and g/sub k+1//sup /spl epsiv//=g/sub k//sup /spl epsiv//+/spl epsiv/(E[Y/sub k//spl psi//sub k+1/]-E[Y/sub k/Y/sub k//sup T/]g/sub k//sup /spl epsiv//) for k=0,1,2,..., the author shows that for any /spl gamma/>0 max/sub 0/spl les/k/spl les//spl gamma//spl epsiv/(-1/)|h/sub k//sup /spl epsiv//-g/sub k//sup /spl epsiv//|/spl rarr/0 as /spl epsiv//spl rarr/0 a.s. and under a stronger dependency condition, the author shows that for any 0</spl zeta//spl les/1 and /spl gamma/>0, max/sub 0/spl les/k/spl les//spl gamma//spl epsiv/(-/spl zeta//)|h/sub k//sup /spl epsiv//-g/sub k//sup /spl epsiv//| converges (as /spl epsiv//spl rarr/0) a.s. At a rate marginally slower than O((/spl epsiv//sup 2-/spl zeta//log log(/spl epsiv//sup -/spl zeta//))/sup 1/2 /). Then, under a stronger pseudostationarity assumption it is shown that similar results hold if the sequences {g/sub k//sup /spl epsiv//}/sub k=0//sup /spl infin//,/spl epsiv/>0 in the above results are replaced with the solution g/sup 0/(/spl middot/) of a nonrandom linear ordinary differential equation, i.e. one has, max/sub 0/spl les/k/spl les/[/spl gamma//spl epsiv/(-/spl zeta//])|h/sub k//sup /spl epsiv//-g/sup 0/(/spl epsiv/k)|/spl rarr/0 as /spl epsiv//spl rarr/0 a.s., where one can attach a rate to this convergence under the stronger dependency condition. The almost-sure bounds contained in the paper complement previously developed weak convergence results in Kushner and Shwartz [IEEE Trans. Information Theory, IT-30(2), 177-182, 1984] and, as are "near optimal". Moreover, the proofs used to establish these bounds are quite elementary.< > |
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| AbstractList | Almost-sure (a.s.) bounds for linear, constant-gain, adaptive filtering algorithms are investigated. For instance, under general pseudo-stationarity and dependence conditions on the driving data {psik,k=1,2,3,...}, {Y(k),k=0,1,2,...} a.s. convergence and rates of a.s. convergence (as the algorithm gain epsilon- > 0) are established for the LMS algorithm h(k 1)(epsilon)=h (k)(epsilon) epsilonY(k)(psi(k 1 )-Y(k)(T)h(k)(epsilon)) subject to some nonrandom initial condition h(0)(epsilon)=h (0). In particular, defining {g(k)(epsilon)} (k=0)({infinity}) by g(0)(epsilon)=h (0) and g(k 1)(epsilon)=g(k)(epsilon ) epsilon(E[Y(k)psi(k 1)]-E[Y(k)Y (k)(T)]g(k)(epsilon)) for k=0,1,2,..., the author shows that for any gamma > 0 max(0 k gammaepsilon(-1/)|h)sub k/(epsilon)-g(k)(epsilon)|- > 0 as epsilon- > 0 a.s. and under a stronger dependency condition, the author shows that for any 0 < zeta 1 and gamma > 0, max(0 k gammaepsilon(-zeta/)|h)sub k/(epsilon )-g(k)(epsilon)| converges (as epsilon- > 0) a.s. At a rate marginally slower than O((epsilon(2-zeta)log log(epsilon(-zeta)))( 1/2)). Then, under a stronger pseudostationarity assumption it is shown that similar results hold if the sequences {g(k)(epsilon)}(k=0) ({infinity}),epsilon > 0 in the above results are replaced with the solution g(0)(.;) of a nonrandom linear ordinary differential equation, i.e. one has, max(0 k [gammaepsilon(-zeta/])|h)sub k/(epsilon )-g(0)(epsilonk)|- > 0 as epsilon- > 0 a.s., where one can attach a rate to this convergence under the stronger dependency condition. The almost-sure bounds contained in the paper complement previously developed weak convergence results in Kushner and Shwartz [IEEE Trans. Information Theory, IT-30(2), 177-182, 1984] and, as are "near optimal". Moreover, the proofs used to establish these bounds are quite elementary Almost-sure bounds for linear, constant-gain, adaptive filtering algorithms are investigated. The bounds presented are "near optimal" and proofs used to establish them are quite elementary. Almost-sure (a.s.) bounds for linear, constant-gain, adaptive filtering algorithms are investigated. For instance, under general pseudo-stationarity and dependence conditions on the driving data {/spl psi/k,k=1,2,3,...}, {Y/sub k/,k=0,1,2,...} a.s. convergence and rates of a.s. convergence (as the algorithm gain /spl epsiv//spl rarr/0) are established for the LMS algorithm h/sub k+1//sup /spl epsiv//=h/sub k//sup /spl epsiv//+/spl epsiv/Y/sub k/(/spl psi//sub k+1/-Y/sub k//sup T/h/sub k//sup /spl epsiv//) subject to some nonrandom initial condition h/sub 0//sup /spl epsiv//=h/sub 0/. In particular, defining {g/sub k//sup /spl epsiv//}/sub k=0//sup /spl infin// by g/sub 0//sup /spl epsiv//=h/sub 0/ and g/sub k+1//sup /spl epsiv//=g/sub k//sup /spl epsiv//+/spl epsiv/(E[Y/sub k//spl psi//sub k+1/]-E[Y/sub k/Y/sub k//sup T/]g/sub k//sup /spl epsiv//) for k=0,1,2,..., the author shows that for any /spl gamma/>0 max/sub 0/spl les/k/spl les//spl gamma//spl epsiv/(-1/)|h/sub k//sup /spl epsiv//-g/sub k//sup /spl epsiv//|/spl rarr/0 as /spl epsiv//spl rarr/0 a.s. and under a stronger dependency condition, the author shows that for any 0</spl zeta//spl les/1 and /spl gamma/>0, max/sub 0/spl les/k/spl les//spl gamma//spl epsiv/(-/spl zeta//)|h/sub k//sup /spl epsiv//-g/sub k//sup /spl epsiv//| converges (as /spl epsiv//spl rarr/0) a.s. At a rate marginally slower than O((/spl epsiv//sup 2-/spl zeta//log log(/spl epsiv//sup -/spl zeta//))/sup 1/2 /). Then, under a stronger pseudostationarity assumption it is shown that similar results hold if the sequences {g/sub k//sup /spl epsiv//}/sub k=0//sup /spl infin//,/spl epsiv/>0 in the above results are replaced with the solution g/sup 0/(/spl middot/) of a nonrandom linear ordinary differential equation, i.e. one has, max/sub 0/spl les/k/spl les/[/spl gamma//spl epsiv/(-/spl zeta//])|h/sub k//sup /spl epsiv//-g/sup 0/(/spl epsiv/k)|/spl rarr/0 as /spl epsiv//spl rarr/0 a.s., where one can attach a rate to this convergence under the stronger dependency condition. The almost-sure bounds contained in the paper complement previously developed weak convergence results in Kushner and Shwartz [IEEE Trans. Information Theory, IT-30(2), 177-182, 1984] and, as are "near optimal". Moreover, the proofs used to establish these bounds are quite elementary.< > |
| Author | Kouritzin, M.A. |
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| References | ref13 ref12 ref15 ref14 sanders (ref18) 1985 ref11 ref22 golub (ref9) 1983 ref21 ref1 ref17 ref16 ref19 stout (ref20) 1974 ref8 ref7 bogoliubov (ref2) 1961 ref4 doukhan (ref5) 1987; 8 ref3 ref6 hall (ref10) 1980 |
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| Snippet | Almost-sure (a.s.) bounds for linear, constant-gain, adaptive filtering algorithms are investigated. For instance, under general pseudo-stationarity and... Almost-sure bounds for linear, constant-gain, adaptive filtering algorithms are investigated. The bounds presented are "near optimal" and proofs used to... |
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| SubjectTerms | Adaptive filters Algorithms Convergence Differential equations Filtering algorithms Information theory Least squares approximation Mathematics |
| Title | On almost-sure bounds for the LMS algorithm |
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