{"paper_id":"1fb14f64-2c33-48db-9fd5-450797493e4d","body_text":"A Study of Correlation Analysis Between Numerical and Experimental Responses of Dynamics-Based Tractor Acceleration and Development of a Correction Algorithm Using AI Regression Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study of Correlation Analysis Between Numerical and Experimental Responses of Dynamics-Based Tractor Acceleration and Development of a Correction Algorithm Using AI Regression Neural Networks Heung Soap Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9446430/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study quantitatively analyzes the dynamic acceleration performance of medium to-large agricultural tractors (60HP and 75HP classes) and validates the reliability of a system-level simulation model through real-vehicle field tests. As the development trend of agricultural machinery shifts toward higher performance and complexity, establishing a virtual verification-based design process is crucial for reducing development period and costs. A comprehensive time-domain dynamic model was constructed using Matlab/Simulink to simulate the entire power transmission path, encompassing the engine polynomial torque curves, sequential gear reduction ratios, and non-linear physical resistance factors. The consistency of this physical model was evaluated against empirical tests conducted strictly under ISO 5721-1 and OECD(Organisation for Economic Co-operation and Development) test codes [1,2]. The physical model demonstrated high reliability, with top speed and target reaching time errors of 1.1% and 3.7%, respectively, satisfying the general performance prediction tolerance of ±5%. A rigorous Design of Experiments (DOE) sensitivity analysis revealed that engine power and vehicle mass are the dominant factors, dictating over 83.4% of the acceleration variance. To minimize residual errors caused by unmodeled non-linearities such as tire slip and transmission delay, a Long Short Term Memory (LSTM) regression neural network was introduced [8]. The AI-based correction algorithm significantly improved prediction precision, reducing the Mean Absolute Percentage Error (MAPE) to within 0.44% and achieving a coefficient of determination (R2) of 0.993. This synergistic physics-data hybrid framework provides a highly robust foundation for digital twin-based performance verification in the off-road vehicle industry [35]. Artificial Intelligence and Machine Learning Agricultural Tractor Dynamic Simulation Acceleration Performance Machine Learning LSTM Bias Correction Model Validation DigitalTwin Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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