Comparison of Neural Networks and Fuzzy System for Estimation of Heat Transfer Between Contacting Surfaces
Publish place: International Journal of Advanced Design and Manufacturing Technology، Vol: 12، Issue: 2
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_ADMTL-12-2_010
تاریخ نمایه سازی: 27 فروردین 1399
Abstract:
Neural networks can be used in various subjects, such as the discovery of relationships, identification, system modelling, optimization and nonlinear pattern recognition. One of the interesting applications of this algorithm is heat transfer estimation between contacting surfaces. In the current investigation, a comparison study is done for temperature transfer function estimation between contacting surfaces using Group Method of Data Handling (GMDH) neural networks and ANFIS (Adaptive Neuro Fuzzy Inference System) algorithm. Different algorithms are trained and tested by means of input–output data set taken from the experimental study and the inverse solution using the Conjugate Gradient Method (CGM) with the adjoint problem. Eventually, the optimal model has been chosen based on the common error criteria of root mean square error. According to the obtained results among different models, ANFIS with gaussmf membership function has the best algorithm for identification of TCC between two contacting surfaces with 0.1283 error. Also, the inverse method has the lowest error for thermal contact conductance estimation between fixed contacting surfaces with root mean square error of 0.211.
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Authors
Shayan Fathi
Department of Mechanical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Mohammad Eftekhari
Department of Mechanical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Arman Adamian
Department of Mechanical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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