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.