A comparison of genetic programming and neural networks; new formulations for electrical resistivity of Zn-Fe alloys
Yükleniyor...
Dosyalar
Tarih
2013
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Heidelberg
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
It is difficult to automatically solve a problem in a systematic method without using computers. In this study, a comparison between Neural Network (NN) and genetic programming (GEP) soft computing techniques as alternative tools for the formulation of electrical resistivity of zinc-iron (Zn-Fe) alloys for various compositions is proposed. Different formulations are supplied to control the verity and robustness of NN and GEP for the formulation to design composition and electrolyte conditions in certain ranges. The input parameters of the NN and GEP models are weight percentages of zinc and iron in the film and in the electrolyte, measurement temperature, and corrosion voltage of the films. The NN- and GEP-based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R (2)=0.998 for GEP and 0.999 for NN).
Açıklama
Anahtar Kelimeler
Electrodeposition, Prediction, Steel, Ni
Kaynak
Applied Physics A-Materials Science & Processing
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
113
Sayı
2
