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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Veröffentlicht in:Alexandria engineering journal Jg. 61; H. 12; S. 12039 - 12050
Hauptverfasser: Magdy Saady, Marina, Hassan Essai, Mohamed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2022
Elsevier
Schlagworte:
Activation function
ANN
Back-propagation
Engine emissions
FPGA
HDL coder
ANN
FPGA
Activation function
Back-propagation
HDL coder
Engine emissions
ISSN:1110-0168
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Zusammenfassung: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.
ISSN:1110-0168
DOI:10.1016/j.aej.2022.05.035