Real-time online detection of trucks loading via genetic neural network

Abstract This article focuses on real-time online detection of trucks loading via genetic neural network. Firstly, according to the state structure of the truck and the deployment of the sensor in the monitoring system, a mathematical model that magnetic sensors detecting the weight of the truck is established, it provides a theoretical basis for the calculation of the compensator deviation. Secondly, a feedback compensator for disturbance signals is designed by genetic neural network in the load monitoring system. Thirdly, the stability of the control system is analyzed by the Lyapunov stability theory. Fourthly, a real-time monitoring system is proposed for the loading of trucks. Finally, a complete experiment is processed to in-depth discussion and analysis. Field experiments showed that this scheme solves the problem of real-time load detection of trucks, it proposes a monitoring system for transportation in the construction industry.

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