Research on automation of support based on genetic algorithm and BP neural network

In view of the problems of frame loss and improper support movement in the automatic process of hydraulic support following machine in fully mechanized mining working, a control method was proposed based on Genetic Algorithm (GA) and BP neural network combined model. Through the establishment of BP neural network controller as the main feedback control, the motion parameters of the support are used as the input of the model. The neural network controller is used to calculate the error between the actual output and the ideal output, to determine whether callback control is required. In order to optimize the thresholds and weights of each layer of the updated model to obtain the optimal solution of the network model, and finally get the optimal solution of the network model, and the execution part completes the output action. The combined network model has good nonlinear characteristics and can better meet the nonlinear environment. The difference between the predicted value of the neural network and the actual output is used to obtain the fitting curve. By analyzing the mean square error (mse) of the BP neural network model, GA model, and BP-GA combined model, it is justified that the GA-BP combined model has faster training speed and higher prediction accuracy. Compared with a single BP neural network model and GA model, the GA-BP combined model can greatly improve the accuracy of the hydraulic support in the process of following the machine, so as to better adapt to the changes in the environment and equipment in the fully mechanized mining working. Based on the analysis of model stability, the fitness curve of the combined model was drawn. The population tends to converge after the 5th iteration, and the fitness value from the 5th to 15th iteration is basically stable, and after the 15th iteration the population has reached the optimal parameter and became constant. The hydraulic support electro-hydraulic control system adopting the above schemecan autonomously sense the changes of various motion parameters of the equipment, realizethe static adjustment and dynamic evolution of the support itself, and provide technical support for the unmanned operation of the fully mechanized mining faces. © 2021 The authors.