期刊
ADVANCES IN DIFFERENCE EQUATIONS
卷 2021, 期 1, 页码 -出版社
SPRINGER
DOI: 10.1186/s13662-021-03537-z
关键词
Fractional order differential equations; Design engineering; Mathematical models; Intelligent computing techniques; Artificial neural networks; Heuristic optimization techniques
资金
- Center of Excellence in Theoretical and Computational Science (TaCS-CoE), KMUTT
In this paper, the problem of temperature distribution for convective straight fins with constant and temperature-dependent thermal conductivity is solved using the ANN-BHCS algorithm, with results showing better performance compared to other analytical techniques. Efficiency of the algorithm was further verified using performance metrics, demonstrating the effectiveness of the ANN-BHCS algorithm.
In this paper, the problem of temperature distribution for convective straight fins with constant and temperature-dependent thermal conductivity is solved by using artificial neural networks trained by the biogeography-based heterogeneous cuckoo search (BHCS) algorithm. We have solved the integer and noninteger order energy balance equation in order to analyse the temperature distribution in convective straight fins. We have compared our results with homotopy perturbation method (HPM), variational iteration method (VIM), and homotopy perturbation Sumudu transform method (HPSTM). The results show that the ANN-BHCS algorithm gives better results than other analytical techniques. We have further checked the efficiency of the ANN-BHCS algorithm by using the performance metrics MAD, TIC, and ENSE. We have calculated the values of MAD, TIC, and ENSE for case 1 of the problem, and histograms of these metrics show the efficiency of our algorithm.
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