Discrete Hopfield neural network approach for crane safety evaluation

The complex mechanical structure and working characteristics of crane determine it is a kind of construction machinery with larger risk factors. In order to ensure the safety and reliability of the crane during operation process, also avoid serious failure which affects the efficiency and progress of the engineering project, this paper uses discrete Hopfield neural network approach to evaluate and monitor the running state of the crane. The experimental results show that the discrete Hopfield neural network mode can accurately evaluate the running state of the crane, and thus provides an effective technical way to improve its security and reliability.