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The BP Neural Network Modeling on Worsted Spinning with Grey Superior Theory and Correlation Analysis

机译:灰色卓越理论与相关性分析的最严格纺纱的BP神经网络建模

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摘要

The characteristic of worsted spinning procedures and the BP neural network modeling technology all have been summarily analyzed. Based on the nonlinear and vague relationship depend on fibers’ performance and craft parameters, the grey superior theory and correlation analysis method are proposed to select more important parameters. Therefore, the input layer node numbers reduce; the network topology structure is simplified that the network’s accuracy and performance are all enhanced greatly. After modeling, the relative mean error percents (MEP) between the predict results and measured value for the yarns’ four quality variables, such as Yarn unevenness, strength, extension at break and ends-down rate, reduce to 2.55%, 2.23%, 2.78% and 1.82% respectively compared to the former 4.56%, 3.35%, 4.24% and 3.95%. The correlation coefficients between them for the four quality variables all have the remarkable enhancement.
机译:已概述了最严格的纺纱程序和BP神经网络建模技术的特点。基于非线性和模糊的关系取决于纤维的性能和工艺参数,提出了灰色优势理论和相关分析方法选择更重要的参数。因此,输入层节点数减少;网络拓扑结构简化了网络的准确性和性能都大大增强。在建模后,预测结果和测量值之间的相对平均误差百分比(MEP)为纱线的四个质量变量,例如纱线不均匀,强度,断裂率的延伸,降低到2.55%,2.23%, 2.78%和1.82%分别与前者4.56%,3.35%,4.24%和3.95%相比。它们之间的相关系数为四个质量变量都具有显着的增强。

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