Adaptive Simulated Annealing for Optimization in Signal Processing Applications

Adaptive Simulated Annealing for Optimization in Signal Processing Applications (1999)

Cached

Download Links

[www.bib.ecs.soton.ac.uk]

by S. Chen , S. Chen Y , B.L. Luk , Portsmouth Po Dj

Citations:

23 - 15 self

Versions

Version History

Metadata Version 2

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Adaptive Simulated Annealing for Optimization in Signal Processing Applications	SVM HeaderParse 0.1
AUTHOR NAME	S. Chen	user correction - Legacy Corrections
AUTHOR NAME	S. Chen Y	user correction - Legacy Corrections
AUTHOR AFFIL	y Department of Electronics and Computer Science; University of Southampton, Highfield; Southampton SO17 1BJ, U.K.; z Department of Electrical and Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	University of Portsmouth, Anglesea Building	user correction - Legacy Corrections
AUTHOR NAME	B.L. Luk	user correction - Legacy Corrections
AUTHOR AFFIL	y Department of Electronics and Computer Science; University of Southampton, Highfield; Southampton SO17 1BJ, U.K.; z Department of Electrical and Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	University of Portsmouth, Anglesea Building	user correction - Legacy Corrections
AUTHOR NAME	Portsmouth Po Dj	user correction - Legacy Corrections
ABSTRACT	Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations. The adaptive simulated annealing (ASA) offers a viable optimization tool for tackling these difficult nonlinear optimization problems. Three applications, maximum likelihood (ML) joint channel and data estimation, infinite-impulse-response (IIR) filter design and evaluation of minimum symbol-error-rate (MSER) decision feedback equalizer (DFE), are used to demonstrate the effectiveness of the ASA. Keywords. Simulated annealing, global optimization, blind equalization, IIR filter, decision feedback equalizer. 1 Introduction Optimization problems with multimodal and/or nonsmooth cost functions are commonly encountered in signal processing applications. Conventional gradient-based algorithms are ineffective in these applications due to the problem of local minima or the difficulty in calculating gradients. Optimization method...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	33 found	ParsCit 1.0