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I. J. Turias, J. M. Gutierrez and P. L. Galindo, “Modeling the Effective Thermal Conductivity of an Unidirectional Composite by the Use of Artificial Neural Networks,” Composites Science and Technology, Vol. 65, No. 3-4, 2005, pp. 609-619. doi:10.1016/j.compscitech.2004.09.018

has been cited by the following article:

• TITLE: Adaptive Neuro-Fuzzy Inference System for Prediction of Effective Thermal Conductivity of Polymer-Matrix Composites

  AUTHORS: Rajpal Singh Bhoopal, Ramvir Singh, Pradeep Kumar Sharma

  KEYWORDS: Neuro-fuzzy Inference System; Effective Thermal Conductivity; Polymer Composites; Volume Fraction; Fuzzy Inference Systems

  JOURNAL NAME: Modeling and Numerical Simulation of Material Science, Vol.2 No.3, July 26, 2012

  ABSTRACT: In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained with data collected from various sources of literature. ETC is predicted using ANFIS with volume fraction and thermal conductivities of fillers and matrixes as input parameters, respectively. The predicted results by ANFIS are in good agreements with experimental values. The predicted results also show the supremacy of ANFIS in comparison with other earlier developed models.