Hot-rolling coiling temperature forecasting method based on relevant neural network

Abstract

The invention provides a hot-rolling coiling temperature forecasting method based on a relevant neural network. Data of strip steel thickness H, rolling velocity V, finish rolling temperature T1, strip steel width W, super rapid cooling valve opening quantity N1, laminar flow valve opening quantity N2, super rapid cooling inlet temperature T2 and target coiling temperature T3 are collected through a personal digital assistant (PDA) terminal, an input matrix [H, V, T1, W, N1, N2, T2, T3] is built, and an output matrix [T] is built. If [V, T1, W, N1, N2, T2, T3] is supposed to be not changed, Y is defined to be an influence degree of the H. According to influence degrees, corresponding influence degrees are given to the weight of the input end and a hidden layer and to the weight of the hidden layer and an output layer, three layers of ASBP neutral networks are built, the actual coiling temperature is output, ASBP neutral network training is conducted, and coiling temperature forecasting is conducted by using actual testing data. By means of the method, a coiling temperature forecasting error range is reduced from -20 DEG C-20 DEG C to -10 DEG C-10 DEG C, and coiling temperature control is more accurate.

Description

technical field [0001] The invention belongs to the field of temperature automatic control, in particular to a method for predicting the coiling temperature of hot rolling based on an associated neural network. Background technique [0002] The hot rolling coiling temperature has a great influence on the physical structure of the strip, and its control accuracy will directly affect the mechanical properties, physical properties and processing properties of the strip. Therefore, ensuring that the coiling temperature of the strip reaches the target control range has always been one of the important issues in the field of hot rolling. However, the coiling temperature control system is a complex system with large lag, multi-variable, strong coupling and strong nonlinearity, and it is difficult to establish an accurate mathematical model for control. [0003] Based on the fusion of the principles of heat conduction and modern technology, many controlled cooling models have been ...

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Problems solved by technology

[0006] The present invention provides a data mining of hot-rolled coiling temperature correlation by using the influence degree standard, assigns different weights to each influencing factor, and solves the problem of inaccurate coiling temperature prediction due to different influence degrees of different factors in the coiling temperature prediction To improve the accuracy of coiling temperature forecasting, a hot rolling coiling temperature forecasting method based on associated neural network

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