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


KARADEDE Y. , ÖZDEMİR G.

SOFT COMPUTING, cilt.22, ss.6937-6964, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 22 Konu: 20
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1007/s00500-018-3413-5
  • Dergi Adı: SOFT COMPUTING
  • Sayfa Sayısı: ss.6937-6964

Özet

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.