A genetic algorithms based technique for computing the nonlinear least squares estimates of the parameters of sum of exponentials model

► We consider least squares estimation of the parameters of sum of exponential model. ► A genetic algorithm based method for finding the nonlinear least squares estimates is proposed. ► The proposed method uses an elitist generational genetic algorithm. ► Simulation studies and real life data analys...

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Bibliographic Details
Published in:Expert systems with applications Vol. 39; no. 7; pp. 6370 - 6379
Main Authors: Mitra, Sharmishtha, Mitra, Amit
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2012
Subjects:
Computation
Elitism
Estimates
Expert systems
Generational genetic algorithms
Genetic algorithms
Least squares method
Mathematical models
Nonlinear least squares estimates
Nonlinearity
Stochastic search
Sum of exponentials model
Time series analysis
Nonlinear least squares estimates
Generational genetic algorithms
Elitism
Sum of exponentials model
Stochastic search
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:► We consider least squares estimation of the parameters of sum of exponential model. ► A genetic algorithm based method for finding the nonlinear least squares estimates is proposed. ► The proposed method uses an elitist generational genetic algorithm. ► Simulation studies and real life data analysis show the usefulness and efficiency of the proposed technique. Estimation of the parameters of a nonlinear sum of exponentials model is an important and well studied problem in time series analysis. The sum of exponentials model finds application in modeling various physical phenomena in a wide variety of real life applications. The problem of finding the nonlinear least squares estimates in well known to be numerically difficult. In this paper, we propose an elitist generational genetic algorithm based iterative procedure for computing the nonlinear least squares estimates. Simulation studies and real life data fitting examples indicate satisfactory performance of the proposed technique.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.12.033