Creating a neural network-based model to predict the exhaust gas temperature of the internal combustion engine
1 Department of Computer Science, Azad University of Sari, Mazandaran, Iran.
2 Department of Mechanical Engineering, University of Gilan, Rasht, Gilan, Iran.
3 Department of Mechanical Engineering, Shomal University, Amol, Mazandaran, Iran.'
Research Article
GSC Advanced Research and Reviews, 2024, 19(01), 079–085.
Article DOI: 10.30574/gscarr.2024.19.1.0147
Publication history:
Received on 07 March 2024; revised on 15 April 2024; accepted on 18 April 2024
Abstract:
The regulation and improvement of the performance of internal combustion engines is a continual primary focus of research and development activities conducted within the automobile industry and other relevant sectors. To succeed in reaching this objective, it is necessary to have an accurate and complete model of these engines. However, due to internal combustion engines' complex and nonlinear nature, accurately replicating their behavior may be challenging and time-consuming. Neural networks are a potentially useful strategy for simulating these engines successfully since they offer a solution that strikes a healthy balance between speed and precision. This research investigates the process of building a model of an internal combustion engine by using not one but two separate kinds of neural networks: multilayer perceptrons and radial basis functions. These neural networks aim to simulate and make predictions about the temperature of the engine's exhaust gas. They are especially useful for modeling nonlinear systems because of their incredible convergence speed and excellent accuracy levels.
Keywords:
Automotive Industry; Internal Combustion Engines; Neural Networks; Modeling; Performance Enhancement; Exhaust Gas Temperature
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
