Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/43115
Title: Experimental and neural network approach to effective electrical conductivity of carbon nanotubes dispersed chiral nematic liquid crystals
Authors: Kumar, Rishi
Singh, R P
Middha, Manju
Raina, K K
Keywords: Single walled carbon nano tubes (SWCNTs);Electrical conductivity;Liquid crystals;Artificial neural network;Electro-optic switching
Issue Date: Nov-2017
Publisher: NISCAIR-CSIR, India
Abstract: Single walled carbon nanotubes (SWCNT’s) doped cholesteric liquid crystal composite has been prepared and characterized for their electrical responses. Also theoretically, an artificial neural network (ANN) approach has been trained for predicting the effective electrical conductivity of these composites. The ANN models are based on a feedforward backpropagation (FFBP) network with such training functions as the adaptive learning rate (GDX), gradient descent with adaptive learning rate (GDA), gradient descent (GD), conjugates gradient with Powell-Beale restarts (CGB), one-step secant (OSS), and Levenberg–Marquardt (LM), and training algorithms run at the uniform threshold transfer functions-Tangent sigmoid (TANSIG) and pure linear (PURELIN) for 1000 epochs. Our modeling confirms that the expected effective electrical conductivity by different training functions of ANN is in higher agreement with the experimental results of SWCNT doped CLC composites.
Page(s): 806-812
URI: http://nopr.niscair.res.in/handle/123456789/43115
ISSN: 0975-0959 (Online); 0301-1208 (Print)
Appears in Collections:IJPAP Vol.55(11) [November 2017]

Files in This Item:
File Description SizeFormat 
IJPAP 55(11) 806-812.pdf494.08 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.