Research on Deep Neural Network Optimization Algorithm in Precision Numerical Control System

Encoder Particle Swarm (EPS) is an innovative approach that combines Autoencoder (AE) and Particle Swarm Optimization (PSO). Research on deep neural network optimization algorithm is applied to precision numerical control system. EPS employs an autoencoder to reduce and extract features from sensor data in precision numerical control systems, and it automatically adjusts network parameters using the particle swarm optimization algorithm to enhance the model's performance and efficiency. This method not only effectively reduces the model's complexity but also improves the precision and response speed of the precision numerical control system. In conclusion, this paper validates the substantial advantages of the introduced deep neural network optimization algorithm in reducing mean square error, root mean square error, and mean absolute error, as evidenced by its comparison with conventional neural network models. The findings underscore that this algorithm effectively addresses the intricacies and uncertainties that traditional scheduling approaches struggle with, thereby demonstrating the efficacy of the model in optimizing and ensuring the feasibility of resource scheduling within precision CNC systems.