Using Multi-input-layer Wavelet Neural Network to Model Product Quality of Continuous Casting Furnace and Hot Rolling Mill |
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作者姓名: | HuanqinLi JieCheng BaiwuWan |
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作者单位: | [1]FacultyofScience,Xi'an]iaotongUniversity,Xi'an710049,China [2]EngineeringCollegeofArmedPoliceForce,Xi'an710086,China [3]SystemsEngineeringInstitute,Xi'anJiaotongUniversity,Xi'an710049,China |
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摘 要: | A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.
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关 键 词: | 小波神经网络 多输入层 高阶维数 工序 产品质量 连续铸造炉 热辗压厂 |
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