中圖分類號(hào)： TP183
文獻(xiàn)標(biāo)識(shí)碼： ADOI：10.16157/j.issn.0258-7998.223210
中文引用格式： 高山鳳，劉美紅，范秋霞. 基于DBN-BP深度算法的熱軋板帶橫斷面預(yù)測(cè)[J].電子技術(shù)應(yīng)用，2022，48(11)：46-50，56.
英文引用格式： Gao Shanfeng，Liu Meihong，F(xiàn)an Qiuxia. Cross sectional shape prediction of hot rolled strip based on DBN-BP deep neural network[J]. Application of Electronic Technique，2022，48(11)：46-50，56.

Cross sectional shape prediction of hot rolled strip based on DBN-BP deep neural network

Abstract： With the rapid development of various industrial fields, the market demand for thin specifications, high strength strip products increases rapidly. The cross-section shape of hot rolled strip is the main evaluation index of hot rolled strip product quality. Based on data mining technology, the data in the mill database are analyzed and processed. The data mining technology combines deep belief neural network(DBN) and back propagation(BP) neural network algorithms to construct a prediction model of strip thickness distribution. The DBN-BP algorithm is composed of several restricted Botlzmann machines(RBM) stacked layer by layer, and the weight matrix and bias of the network are obtained by unsupervised layer-by-layer training method for the BP network, while the BP neural network fine-tunes the whole network by means of error back propagation. This method overcomes the disadvantages of BP network falling into local optimum due to random initialization of weight parameters and long training time. Compared with the BP algorithm, the probability of predicting the midpoint thickness error is within ±5.6 μm by the DBN-BP method is 95%, while the prediction error of BP algorithm is within ±11 μm. Through the analysis of the prediction results of the cross-section shape of the strip, it can be seen that the DBN-BP deep learning method has more advantages than the BP algorithm in predicting the edge thickness of the strip.

Key words : hot rolling；deep learning；strip thickness prediction