Hardware implementation of neural network-based engine model using FPGA
This paper implements an artificial neural network (ANN)-based engine model using the Field Programmable Gate Array (FPGA). The developed (ANN)-based engine model will be used to estimate the engine gas emissions to mitigate the harmful effects of these emissions on human health. Getting reliable an...
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| Vydáno v: | Alexandria engineering journal Ročník 61; číslo 12; s. 12039 - 12050 |
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| Jazyk: | angličtina |
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Elsevier B.V
01.12.2022
Elsevier |
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| ISSN: | 1110-0168 |
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| Abstract | This paper implements an artificial neural network (ANN)-based engine model using the Field Programmable Gate Array (FPGA). The developed (ANN)-based engine model will be used to estimate the engine gas emissions to mitigate the harmful effects of these emissions on human health. Getting reliable and robust FPGA-based ANNs implementations depends on the optimal choice of activation function that will provide minimal area occupation on FPGA. This study introduces, implements, and investigates FPGA-based ANN-based engine models using five different activation functions. These implemented engine models were described using MATLAB/Simulink and hardware description language coder and carried out by Spartan -3E-500.CP132 FPGA platform from Xilinx. The performance of the implemented engine models was investigated in terms of area-efficient implementation and the regression values (R) to build a robust ANN-based engine model. |
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| AbstractList | This paper implements an artificial neural network (ANN)-based engine model using the Field Programmable Gate Array (FPGA). The developed (ANN)-based engine model will be used to estimate the engine gas emissions to mitigate the harmful effects of these emissions on human health. Getting reliable and robust FPGA-based ANNs implementations depends on the optimal choice of activation function that will provide minimal area occupation on FPGA. This study introduces, implements, and investigates FPGA-based ANN-based engine models using five different activation functions. These implemented engine models were described using MATLAB/Simulink and hardware description language coder and carried out by Spartan -3E-500.CP132 FPGA platform from Xilinx. The performance of the implemented engine models was investigated in terms of area-efficient implementation and the regression values (R) to build a robust ANN-based engine model. |
| Author | Hassan Essai, Mohamed Magdy Saady, Marina |
| Author_xml | – sequence: 1 givenname: Marina surname: Magdy Saady fullname: Magdy Saady, Marina email: eng.rena201333@gmail.com organization: Electronics & Communications Engineering Department, Higher Institute of Engineering and Technology, Luxor – El Tod, Egypt – sequence: 2 givenname: Mohamed surname: Hassan Essai fullname: Hassan Essai, Mohamed organization: Electrical Engineering Department, Faculty of Engineering-Qena, Al-Azhar University, Egypt |
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| Cites_doi | 10.1109/TITS.2020.3018054 10.1016/j.compeleceng.2022.107725 10.1016/j.aej.2020.10.012 10.1162/NECO_a_00849 10.1109/TITS.2021.3109846 10.1109/ICITEED.2016.7863293 10.3390/computation7040063 10.1016/j.apenergy.2011.08.027 10.1109/54.953280 10.1007/s40430-020-02666-y 10.1016/j.enconman.2020.112520 |
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| Keywords | ANN FPGA Activation function Back-propagation HDL coder Engine emissions |
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(IJTIR) – start-page: 37 year: 2006 ident: 10.1016/j.aej.2022.05.035_b0105 article-title: On the Arithmetic Precision for Implementing Back-Propagation Networks on FPGA: A Case Study – volume: 3 year: 2021 ident: 10.1016/j.aej.2022.05.035_b0015 article-title: Prediction efficiency of artificial neural network for CRDI engine output parameters publication-title: Transport. Eng. – volume: 207 start-page: 112520 year: 2020 ident: 10.1016/j.aej.2022.05.035_b0005 article-title: Analysis of significance of variables in IC engine operation: an empirical methodology publication-title: Energy Convers. Manage. doi: 10.1016/j.enconman.2020.112520 – year: 2000 ident: 10.1016/j.aej.2022.05.035_b0045 – ident: 10.1016/j.aej.2022.05.035_b0050 – volume: 32 start-page: 165 year: 2018 ident: 10.1016/j.aej.2022.05.035_b0025 article-title: Mathematical modelling of diesel engine operational performance parameters in transient modes publication-title: Sci. J. Maritime Res. |
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| Title | Hardware implementation of neural network-based engine model using FPGA |
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