A comparative study for the estimation of parameters in nonlinear models

Kuldeep Kumar, Mohammed A Alsaleh

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

The most commonly used numerical optimization techniques include the methods of Gauss-Newton, Newton-Raphson, gradient methods, including methods of steepest ascent and descent, and Marquardt algorithm. Kumar [1] has recently proposed a new technique based on optimum exponential regression. Another noniterative procedure proposed in this paper is based on the principle of internal regression. In this paper, we have compared these methods using real data sets.
Original languageEnglish
Pages (from-to)179-184
Number of pages6
JournalApplied Mathematics and Computation
Volume77
Issue number2-3
DOIs
Publication statusPublished - 1 Jan 1996

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Gradient methods
Comparative Study
Nonlinear Model
Regression
Gauss-Newton
Newton-Raphson
Ascent
Numerical Optimization
Gradient Method
Numerical Techniques
Descent
Optimization Techniques
Internal

Cite this

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A comparative study for the estimation of parameters in nonlinear models. / Kumar, Kuldeep; Alsaleh, Mohammed A.

In: Applied Mathematics and Computation, Vol. 77, No. 2-3, 01.01.1996, p. 179-184.

Research output: Contribution to journalArticleResearchpeer-review

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