Prediction and comparison of emitted radiation from a flat plate heat sink using linear regression analysis with artificial neural network and ANFIS

Abstract

This paper provides a comparison of multiple linear regression analysis with artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), for predicting the emitted radiations from a flat plate heat sink. HFSS simulations were designed using Ansoft version 12 for the L27 orthogonal array and optimised using Taguchi design of experiments method. The heat sink geometry factors considered for the L27 (six factors, three levels) design are length, width, fin height, base height, fin thickness and number of fins and the response studied is the emitted radiation from the heat sink. A meta model is developed based on a multiple linear regression method using HFSS simulations. Also, the results of L27 orthogonal array were used to train the artificial neural network and the ANFIS-based intelligent networks. The accuracy of results for the prediction of emitted radiations using multiple linear regressions, ANN and ANFIS were compared with HFSS simulations. From the results, it is found that the ANFIS outperforms ANN and regression models for the prediction of the emitted radiations from the heat sink.