Strip steel exit thickness prediction method by symmetric extreme learning machine (Sym-ELM) optimized by random frog leaping algorithm
An extreme learning machine, export thickness technology, applied in machine learning, prediction, computing and other directions, can solve problems such as ineffective utilization and affect generalization, so as to improve generalization performance, improve prediction performance, and reduce prediction errors. Effect
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[0119] Example 1:
[0120] The random leapfrog optimization symmetric extreme learning machine of the present invention is used in the prediction of strip steel exit thickness, and the results are compared with the traditional extreme learning machine to verify the effectiveness of the present invention. The specific steps are as follows:
[0121] 1. Analyze the collected strip steel data signals. The strip steel experiment data comes from the real-time signal data collected by a domestic steel mill in the actual strip rolling process. In the process of strip rolling, the strip rolling unit consists of 9 rolling stands, and the various parameters of each stand will have a certain effect on the thickness of the strip. The original strip data is stored in the form of signals, and the rolling force, rolling speed, motor current, rolling force, roll gap and other data signals that affect it are analyzed using ibaAnalyzer data analysis software. Extract several main factors that can a...
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