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Defect recognition of cold rolled plate shape based on RBF-BP neural network

机译:基于RBF-BP神经网络的冷轧板形缺陷识别

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By means of the analysis for the defect pattern of plate shape, a shape defect recognition method for cold rolled strips is proposed based on RBF-BP neural network in this paper. The memberships relative to six basic patterns of common plate shape defects are identified. This method syncretizes the advantages of RBF and BP neural network. There are very fast approaching speed and high precision of network recognition. The simulation of the proposed method is done, and the simulation results are compared with the results of the recognition method by using BP neural network. The results show that the recognition method proposed in this paper gives better effect than the one making use of single network. And it is more suitable for real-time shape control.
机译:通过对板状缺陷图案的分析,提出了一种基于RBF-BP神经网络的冷轧板形缺陷识别方法。确定了与常见的板状缺陷的六个基本图案有关的成员资格。该方法融合了RBF和BP神经网络的优势。网络识别具有非常快的接近速度和高精度。对提出的方法进行了仿真,并通过BP神经网络将仿真结果与识别方法的结果进行了比较。结果表明,本文提出的识别方法比单一网络识别方法具有更好的识别效果。并且它更适合于实时形状控制。

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