Surface Mounted Production Line Capacity Prediction Based on Data Driven

Abstract Capacity prediction of surface mount production lines is important for improving productivity, dynamic scheduling, and optimizing structure. An integrated mutual information-cuckoo search-extreme learning machine (MI-CS-ELM) model proposed the impact nonlinear high-dimensional features surface-mounted on capacity. Firstly, affecting capacity are ranked based information method, predictive input data set reconstructed. Secondly, parameters adjusted by cuckoo search algorithm built an extreme limit machine. Finally, rationality method verified example to guide planning scheduling optimization.