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Uncertain Machine Load Forecasting Based on Least Squares Support Vector Machine
Qiaofeng Meng
255 - 261
10.3233/FAIA210410
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 345: Proceedings of CECNet 2021
Abstract

Machine state is a very important constraint for job shop scheduling. For the uncertainty machine state, the paper proposes a machine load forecasting method based on support vector machine. The method reduces complexity and improves efficiency by eliminating a large number of unrelated input factors and selecting a small number of input parameters with strong correlation. The efficiency of the algorithm is verified by the production workshop instance.

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