A novel method to produce nonlinear empirical physical formulas for experimental nonlinear electro-optical responses of doped nematic liquid crystals: Feedforward neural network approach


YILDIZ N. , SAN S. E. , Okutan M., Kaya H.

Physica B: Condensed Matter, cilt.405, ss.2049-2056, 2010 (SCI Expanded İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 405 Konu: 8
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.physb.2010.01.100
  • Dergi Adı: Physica B: Condensed Matter
  • Sayfa Sayıları: ss.2049-2056

Özet

Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs. (C) 2010 Elsevier B.V. All rights reserved.