Prediction and comparison of emitted radiation from a flat plate heat sink using linear regression analysis with artificial neural network and ANFIS. (1st January 2011)

• Record Type: Journal Article
• Authors: Manivannan, S.; Devi, S. Prasanna; Arumugam, R.; Paramasivam, S.; Sudharsan, N.M.
• Is Part Of: International journal of instrumentation technology. Volume 1:Number 1(2011)
• Page Start: 18
• Page End: 33
• Publication Date: 2011-01-01
• Subjects: emitted radiation -- heat sink -- multiple linear regression analysis -- MLRA -- artificial neural networks -- ANN -- adaptive neuro fuzzy inference system -- ANFIS; Engineering instruments -- Periodicals; Automatic control -- Periodicals; 629.805
• Languages: English
• ISSNs: 2043-7854

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.