On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

Author

Al-Naffouri, Tareqy ; Sayed, Ali H. ; Kailath, Thomas

Author_Institution

Dept. of Electr. Eng., Stanford Univ., CA, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

464

Abstract

This paper derives an expression for the optimal error nonlinearity in adaptive filter design. Using an energy conservation relation, and some typical assumptions, the choice of the error function is optimized by minimizing the mean-square deviation subject to a fixed rate of convergence. The resulting optimal choice is shown to subsume earlier results as special cases

Keywords

adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; gradient methods; least mean squares methods; optimisation; stochastic processes; adaptive filter design; energy conservation relation; error function; fixed convergence rate; mean-square deviation minimization; optimal error nonlinearity; optimal nonlinearities selection; second-order analysis; stochastic gradient adaptive algorithms; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Design engineering; Energy conservation; Estimation error; Noise measurement; Power engineering and energy; Steady-state; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.862014

Filename

862014