NARX Technique to Predict Torque in Internal Combustion Engines
نویسندگان
چکیده
To carry out increasingly sophisticated checks, which comply with international regulations and stringent constraints, on-board computational systems are called upon to manipulate a growing number of variables, provided by an ever-increasing real virtual sensors. The optimization phase ICE passes through the control these numerous often exhibit rapidly changing trends over time. On one hand, amount data be processed, narrow cyclical frequencies, entails ever more powerful equipment. other strategies techniques required allow actuation times that useful for timely optimized control. In automotive industry, ‘machine learning’ approach is becoming most used approaches perform forecasting activities reduced effort, due both its cost-effectiveness simple compact structure. present work, nonlinear dynamic system we address related torque estimation autoregressive exogenous inputs (NARX) approach. Preliminary were performed optimize neural network in terms neurons, hidden layers, input parameters assessed. A Shapley sensitivity analysis allowed quantification impact each variable on target prediction, therefore, reduction processed architecture. all cases analyzed, structure was able achieve average percentage errors prediction always lower than critical threshold 10%. particular, when dataset augmented or analyzed merged, architecture achieved about 1%, highlighting remarkable ability reproduce fidelity.
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ژورنال
عنوان ژورنال: Information
سال: 2023
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info14070417