Optimization of Robot Control Model Based on Bald Eagle Search Algorithm and BP Neural Network
In order to achieve precise control of robots,this paper uses the bald eagle search algorithm to optimize the threshold and weight parameters of the BP neural network,and proposes a robot control model based on the bald eagle search algorithm to optimize the BP neural network.The control performance of the model was compared with BP neural network and extreme learning machine,and the results showed that the accuracy of the BES-BP model's control results was 100％,with a calculation time of 181.37 seconds.Both accuracy and calculation time were better than the two comparison methods,verifying the practicality and superiority of the BES-BP model in robot control.
万方数据
维普
