PSO (Particle Swarm Optimization) extremity learning machine based strip steel exit thickness predicting method

Abstract

The invention relates to a PSO (Particle Swarm Optimization) extremity learning machine based strip steel exit thickness predicting method, which basically comprises the steps below: 1) analyzing a strip steel data signal by utilizing a data processing software, selecting four parameters which greatly influence the thickness of the strip steel exit and includes a roll force, a roll gap, a roll speed and a motor current, and inputting the four parameters as input variables into an extremity learning machine in the prediction of the thickness of the strip steel exit; 2) performing selective optimization on parameter input weights and a hidden layer offset value in the extremity learning machine by using the PSO, analyzing and determining output weights by applying a generalized inverse way to obtain an output weight matrix with a minimum norm value in the extremity learning machine, and accordingly obtain optimal parameters of the extremity learning machine; 3) modeling the obtained optimal extremity learning machine; 4) predicting the thickness of the strip steel exit by inputting the four parameters in the step 1) into the optimized extremity learning machine. By applying the PSO extremity learning machine based strip steel exit thickness predicting method, analysis aiming at the rolling production process is carried out, the prediction for the thickness of a rolled piece exit is performed, relevant technical parameters affecting the quality of the strip steel are further analyzed, and real-time control and adjustment for the rolling production process are further carried out.

Description

technical field [0001] The invention relates to a method for predicting the thickness of a steel strip exit, in particular to a method for predicting the thickness of a strip steel exit based on a particle swarm optimization extreme learning machine. Background technique [0002] Strip thickness plays an important role in the rolling process, and the accuracy of exit thickness has become an important index to measure the quality of finished strip steel, and has received extensive attention from the metallurgical industry at home and abroad. However, in the actual rolling process, there are many factors affecting the thickness of the strip exit, and each factor has different effects on the thickness of the strip according to the tension control method. At present, the Automatic Gauge Control (AGC) method is widely used in the prediction of strip thickness control, but the control accuracy of this method depends entirely on the accuracy of the control model, so the thickness p...

AI Technical Summary

Problems solved by technology

However, because its input weights and hidden layer bias values ​​are randomly selected, the extreme learning machine needs more hidden layer nodes than the traditional gradient-based learning algorithm, and it is easy to cause the system to be ill-conditioned. In order to solve Many scholars at home and abroad have conducted further research on extreme learning machines for this kind of problem.

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