Novel method of flatness pattern recognition via cloud neural network

被引:10
|
作者
Zhang, Xiu-ling [1 ,2 ]
Zhao, Liang [3 ]
Zhao, Wen-bao [3 ]
Xu, Teng [3 ]
机构
[1] Yanshan Univ, Key Lab Ind Comp Control Engn Hebei Prov, Qinhuangdao 066004, Peoples R China
[2] Natl Engn Res Ctr Equipment & Technol Cold StripR, Qinhuangdao 066004, Peoples R China
[3] Yanshan Univ, Key Lab Ind Comp Control Engn Hebei Prov, Qinhuangdao 066004, Peoples R China
关键词
Neural network; Cloud model; Flatness pattern recognition; Fuzziness; Randomness; SIMULATION; SHAPE;
D O I
10.1007/s00500-014-1445-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Aiming at the weakness of the existing cloud neural network on training and practicality, a new improved structure of cloud neural network is designed. A hidden layer is added prior to the inverse cloud layer. Threshold level is set to zero and a simple training method is designed. In addition, considering the ignorance of signal randomness and fuzziness in the existing method of the flatness signal recognition, the cloud neural network combines the advantages of the fuzziness and randomness of cloud model and the learning and memory ability of neural network. Thus it is applied in the flatness signal recognition. The simulation contrast results demonstrate that the improved structure is able to identify common defects in shape with higher identity precision.
引用
收藏
页码:2837 / 2843
页数:7
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