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Soft Computing

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30.07.2018 | Methodologies and Application

A hierarchical soft computing model for parameter estimation of curve fitting problems

verfasst von: Yusuf Karadede, Gültekin Özdemir

Erschienen in: Soft Computing | Ausgabe 20/2018

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Abstract

The aim of this paper is to present an alternative solution model to estimate the coefficients of large-scaled linear and nonlinear real-life problems due to the fact that least squares and least median squares parameter estimators have some drawbacks when including so many input variables or increased size of the real-world problems. The study presents a hierarchical soft computing model (SOFTC) that consists of three stages. The first stage constitutes a real-valued breeder genetic algorithm (RVBGA). The second stage is constructing a simulated annealing (SA) algorithm in which the best parameter estimation of the RVBGA is selected as its initial point. The third stage is developing a hierarchical soft computing model by using fuzzy recombination method. SOFTC optimizes the best parameter estimations of this algorithms and it provides a trust region for parameter estimation. Three test problems, one of which is linear and others are nonlinear, are used to examine robustness of proposed models. SOFTC, RVBGA_SA and RVBGA algorithms performed the best parameter estimations, respectively, for the three test problems. The results which are discussed in detail are promising for future usage of these algorithms.

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Titel: A hierarchical soft computing model for parameter estimation of curve fitting problems
verfasst von: Yusuf Karadede
Gültekin Özdemir
Publikationsdatum: 30.07.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 20/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3413-5

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