两轮车燃料预测中的机器学习算法

P. Ranjana, S. Sridevi, T. Sudalai Muthu, V. V. Gnanaraj
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引用次数: 2

摘要

在当今数字化世界中,车队管理是通过固定燃料和固定实验室条件在两轮车上完成的。但在现实世界中,里程预测会根据驾驶员的驾驶风格、驾驶速度、道路状况、交通状况等各种因素而发生变化。因此,为了有效地管理车队,我们建立了一个机器学习多特征回归模型,以预测两轮车在可用燃料下行驶的距离。该系统采用安装在两轮车和油箱上的传感器进行设计,将传感器获取的值应用到回归模型中,实时预测行驶里程,精度更高。
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Machine Learning Algorithm in Two wheelers fuel Prediction
In the present digitized world fleet management is done on the two wheelers by fixing the fuel with fixed laboratory condition. But in the real world, the mileage prediction will change based on various factors like the driving style of the driver, driving speed, road condition, traffic condition etc. So to have an effective fleet management a Machine learning multi feature regression is modeled to predict the distance to be travelled by the two wheelers with the available fuel. It is designed using the sensors placed on the two wheelers and the petrol tank, through which the values obtained through the sensors are applied on regression model to predict the mileage in real time with more accuracy.
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