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The Research and Implementation of Production Prediction and Controlling Based on Data Mining Algorithm

Author: MaWenBo
Tutor: WangBai
School: Beijing University of Posts and Telecommunications
Course: Computer Science and Technology
Keywords: Hot strip mill products BP neural network Radial basis function neural network Sequential pattern mining
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 56
Quote: 0
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Abstract


At this stage, the rapid development of China's iron and steel industry in the international advanced level in the production. However, the accuracy of the product, production efficiency there is still a large gap with the world advanced level. In order to meet the market demand, to improve competitiveness, in recent years, major companies are focusing on the production of high-precision high-quality steel products. Hot Strip Mill production control is a complex steel manufacturing process, there is the complexity of the physical changes and phase transitions change. Hot rolling production process mainly consists of heating the slab, rough rolling, finish rolling, cooling, and winding a few most, which each process are related to the types of factors that affect the final quality and performance indicators. The purpose of this project is to find a suitable data mining algorithms play a guiding role in the production of hot strip mill products. The data used in the experimental data are derived from the actual steel manufacturers in the production of hot strip mill products extracted data, including from steel plates slab heating to the final production of the extract to the finished production process contains product information , as well as the performance of the final product examine data. Hot strip mill production data from several angles, to achieve the effect of product performance to predict timing production sequence analysis: 1, first proposed using BP neural network to analyze the data and achieve product performance prediction purposes. BP neural network in dealing with complex nonlinear data it has better nonlinear approximation ability. But while noting that BP neural network also has its limitations, and how to determine the hidden layer node number, what training methods, training longer issue has not been a good solution. 2, taking into account some of the drawbacks of the BP neural network, this paper, the radial basis function neural network used in the analysis of iron and steel products, and at the same time, the associations found that the combination of thinking and radial basis function neural network method. Radial basis function neural network, the general structure is similar to the BP neural network, are multi-layer neurons through the weights connected to form the network, both major difference is that the radial basis function neural network using cluster analysis methods to determine the hidden layer nodes, this can be overcome by the BP neural network is not stable enough, and the shortcomings of the training slower. This paper introduces Societies found hidden layer nodes to achieve a higher prediction accuracy. 3, the analysis found that more than two neural network methods did not consider the time factor, hot rolling production process has timing, this paper, using time series pattern mining method to analyze the impact of the factors of production of the final product performance in each of the categories .

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