The Start of Combustion Prediction for Methane-Fueled HCCI Engines: Traditional vs. Machine Learning Methods
نویسندگان
چکیده
In this work, 11 regression models based on machine learning techniques were employed to provide a fast-response and accurate model for the prediction of start combustion in homogeneous charge compression ignition engines fueled with methane. These are categorized into linear nonlinear types. Although robust random sample consensus (RANSAC) is type as well SAM (simple algebraic model), accuracy enhanced from 89.3% 98.4%. Such also achieved models, namely, ordinary least squares, ridge, Bayesian ridge models. Indeed, due hypothesis (the correlation prediction), presented have an acceptable response time be used real-time control applications like electronic units engines.
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ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2022
ISSN: ['1026-7077', '1563-5147', '1024-123X']
DOI: https://doi.org/10.1155/2022/4589160