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A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network 被引量:2
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作者 he hai-tao li yan 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2008年第5期32-36,共5页
In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and lo... In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC (cerebellar model articulation controllers) neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved. 展开更多
关键词 FLATNESS pattern recognition CMAC neural network fuzzy distance
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