Using a Stochastic Funnel to find NLR Starting Values

Adrian O'Connor; Junbin Gao; John Louis

Using a Stochastic Funnel to find NLR Starting Values

Adrian O'Connor, Junbin Gao, John Louis

Computing and Mathematics

Research output: Book chapter/Published conference paper › Conference paper

1 Citation (Scopus)

50 Downloads (Pure)

Abstract

This presentation introduces a new evolutionary algorithm that can be used to find starting values (SVs)for nonlinear regression (NLR) problems. The method is called a Stochastic Funnel Algorithm (SFA). The SFA is introduced through its operation in the SSE/parameter space of a NLR model. The SFA uses a relaxed trust region in the log10 space of the parameters of an NLR model. The SFA takes explicit advantage of the general shape of the Sum-of-Squares Error (SSE) of a data/model set to explore and exploit that space and eventually converge to the region of the best parameter estimates.

Original language	English
Title of host publication	WORLDCOMP'08
Subtitle of host publication	World Congress in Computer Science, Computer Engineering and Applied Computing
Editors	Hamid R Arabnia, Youngsong Mun
Place of Publication	USA
Publisher	CSREA Press
Pages	96-102
Number of pages	7
ISBN (Electronic)	1601320698
Publication status	Published - 2008
Event	International Conference on Genetic and Evolutionary Methods (GEM) - Las Vegas, Nevada, USA, New Zealand Duration: 14 Jul 2008 → 17 Jul 2008

Conference

Conference	International Conference on Genetic and Evolutionary Methods (GEM)
Country	New Zealand
Period	14/07/08 → 17/07/08

Access to Document

7861 published paperAccepted author manuscript, 346 KB

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Cite this

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title = "Using a Stochastic Funnel to find NLR Starting Values",

abstract = "This presentation introduces a new evolutionary algorithm that can be used to find starting values (SVs)for nonlinear regression (NLR) problems. The method is called a Stochastic Funnel Algorithm (SFA). The SFA is introduced through its operation in the SSE/parameter space of a NLR model. The SFA uses a relaxed trust region in the log10 space of the parameters of an NLR model. The SFA takes explicit advantage of the general shape of the Sum-of-Squares Error (SSE) of a data/model set to explore and exploit that space and eventually converge to the region of the best parameter estimates.",

keywords = "Open access version available, Evolutionary Computation, Stochastic Funnel Algorithm",

author = "Adrian O'Connor and Junbin Gao and John Louis",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: CSREA Press, 2008. editor/s (773b) = Hamid R Arabnia and Youngsong Mun; Event dates (773o) = 14-17 July 2008; Parent title (773t) = International Conference on Genetic and Evolutionary Methods (GEM).; International Conference on Genetic and Evolutionary Methods (GEM) ; Conference date: 14-07-2008 Through 17-07-2008",

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language = "English",

pages = "96--102",

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booktitle = "WORLDCOMP'08",

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T1 - Using a Stochastic Funnel to find NLR Starting Values

AU - O'Connor, Adrian

AU - Gao, Junbin

AU - Louis, John

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: CSREA Press, 2008. editor/s (773b) = Hamid R Arabnia and Youngsong Mun; Event dates (773o) = 14-17 July 2008; Parent title (773t) = International Conference on Genetic and Evolutionary Methods (GEM).

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N2 - This presentation introduces a new evolutionary algorithm that can be used to find starting values (SVs)for nonlinear regression (NLR) problems. The method is called a Stochastic Funnel Algorithm (SFA). The SFA is introduced through its operation in the SSE/parameter space of a NLR model. The SFA uses a relaxed trust region in the log10 space of the parameters of an NLR model. The SFA takes explicit advantage of the general shape of the Sum-of-Squares Error (SSE) of a data/model set to explore and exploit that space and eventually converge to the region of the best parameter estimates.

AB - This presentation introduces a new evolutionary algorithm that can be used to find starting values (SVs)for nonlinear regression (NLR) problems. The method is called a Stochastic Funnel Algorithm (SFA). The SFA is introduced through its operation in the SSE/parameter space of a NLR model. The SFA uses a relaxed trust region in the log10 space of the parameters of an NLR model. The SFA takes explicit advantage of the general shape of the Sum-of-Squares Error (SSE) of a data/model set to explore and exploit that space and eventually converge to the region of the best parameter estimates.

KW - Open access version available

KW - Evolutionary Computation

KW - Stochastic Funnel Algorithm

M3 - Conference paper

SP - 96

EP - 102

BT - WORLDCOMP'08

A2 - Arabnia, Hamid R

A2 - Mun, Youngsong

PB - CSREA Press

CY - USA

T2 - International Conference on Genetic and Evolutionary Methods (GEM)

Y2 - 14 July 2008 through 17 July 2008

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