Nonlinear experimental dye-doped nematic liquid crystal optical transmission spectra estimated by neural network empirical physical formulas

• Published in: Optics communications Vol. 283; no. 17; pp. 3271 - 3278
• Main Authors: Yildiz, Nihat, San, Sait Eren, Köysal, Oğuz
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2010; Elsevier
• Subjects: Computers in experimental physics; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Instruments, apparatus, components and techniques common to several branches of physics and astronomy; Liquid crystals; Nematic liquid crystal; Neural network; Neural networks, fuzzy logic, artificial intelligence; Nonlinearity; Optical materials; Optics; Physics; Transmission; Nonlinearity; Neural network; Nematic liquid crystal; Transmission; Neural networks; Ambient temperature; Halogen lamp; Response functions; Liquid crystals; Nematic crystals; Transmittance
• ISSN: 0030-4018; 1873-0310
• DOI: 10.1016/j.optcom.2010.04.035

In this paper, two complementary objectives related to optical transmission spectra of nematic liquid crystals (NLCs) were achieved. First, at room temperature, for both pure and dye (DR9) doped E7 NLCs, the 10–250 W halogen lamp transmission spectra (wavelength 400–1200 nm) were measured at various bias voltages. Second, because the measured spectra were inherently highly nonlinear, it was difficult to construct explicit empirical physical formulas (EPFs) to employ as transmittance functions. To avoid this difficulty, layered feedforward neural networks (LFNNs) were used to construct explicit EPFs for these theoretically unknown nonlinear NLC transmittance functions. As we theoretically showed in a previous work, a LFNN, as an excellent nonlinear function approximator, is highly relevant to EPF construction. The LFNN-EPFs efficiently and consistently estimated both the measured and yet-to-be-measured nonlinear transmittance response values. The experimentally obtained doping ratio dependencies and applied bias voltage responses of transmittance were also confirmed by LFFN-EPFs. This clearly indicates that physical laws embedded in the physical data can be faithfully extracted by the suitable LFNNs. The extraordinary success achieved with LFNN here suggests two potential applications. First, although not attempted here, these LFNN-EPFs, by such mathematical operations as derivation, integration, minimization etc., can be used to obtain further transmittance related functions of NLCs. Second, for a given NLC response function, whose theoretical nonlinear functional form is yet unknown, a suitable experimental data based LFNN-EPF can be constructed to predict the yet-to-be-measured values.