Structured stochastic optimization strategies for problems with ill-conditioned error surfaces

Siddharth Pal; D. J. Krusienski; William Kenneth Jenkins

doi:10.1109/ISCAS.2005.1465081

Structured stochastic optimization strategies for problems with ill-conditioned error surfaces

Siddharth Pal, D. J. Krusienski, William Kenneth Jenkins

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

This paper compares the performance of several structured optimization strategies in adaptive signal processing problems that are characterized by ill-conditioned error surfaces. The genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a new constrained random search (CRS) algorithm [8] are considered. When applied to adaptive filters, these structured stochastic search strategies re independent of the adaptive filter structure and are capable of converging to the global solution when applied in circumstances that create multi-modal mean squared error surfaces.

Original language	English (US)
Article number	1465081
Pages (from-to)	2291-2294
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/ISCAS.2005.1465081
State	Published - Dec 1 2005
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: May 23 2005 → May 26 2005

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/ISCAS.2005.1465081

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title = "Structured stochastic optimization strategies for problems with ill-conditioned error surfaces",

abstract = "This paper compares the performance of several structured optimization strategies in adaptive signal processing problems that are characterized by ill-conditioned error surfaces. The genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a new constrained random search (CRS) algorithm [8] are considered. When applied to adaptive filters, these structured stochastic search strategies re independent of the adaptive filter structure and are capable of converging to the global solution when applied in circumstances that create multi-modal mean squared error surfaces.",

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AU - Krusienski, D. J.

AU - Jenkins, William Kenneth

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N2 - This paper compares the performance of several structured optimization strategies in adaptive signal processing problems that are characterized by ill-conditioned error surfaces. The genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a new constrained random search (CRS) algorithm [8] are considered. When applied to adaptive filters, these structured stochastic search strategies re independent of the adaptive filter structure and are capable of converging to the global solution when applied in circumstances that create multi-modal mean squared error surfaces.

AB - This paper compares the performance of several structured optimization strategies in adaptive signal processing problems that are characterized by ill-conditioned error surfaces. The genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a new constrained random search (CRS) algorithm [8] are considered. When applied to adaptive filters, these structured stochastic search strategies re independent of the adaptive filter structure and are capable of converging to the global solution when applied in circumstances that create multi-modal mean squared error surfaces.

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M3 - Conference article

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EP - 2294

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

M1 - 1465081

T2 - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

Y2 - 23 May 2005 through 26 May 2005

ER -

Structured stochastic optimization strategies for problems with ill-conditioned error surfaces

Abstract

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