A new flatness pattern recognition model based on cerebellar model articulation controllers network

  • Hai-tao HeEmail author
  • Yan Li


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 improved.

Key words

flatness pattern recognition CMAC neural network fuzzy distance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    PENG Yan, LIU Hong-min. Pattern Recognition Method Progress of Measured Signals of Shape in Cold Rolling [J]. Journal of Yanshan University, 2003, 27(2): 142 (in Chinese).Google Scholar
  2. 2.
    ZHANG Xiu-ling, LIU Hong-min. Pattern Recognition of Shape Signal by Variable Structure Neural Network [J]. Journal of Iron and Steel Research, 2001, 13(2): 62 (in Chinese).CrossRefGoogle Scholar
  3. 3.
    ZHANG Xiu-ling, LIU Hong-min. GA-BP Model of Flatness Pattern Recognition and Improved Least Squares Method [J]. Iron and Steel, 2003, 38(10): 29 (in Chinese).Google Scholar
  4. 4.
    Albus J S. A New Approach to Manipulator Control, the Cerebellar Model Articulation Controller (CMAC) [J]. Transactions of the ASME, Journal of Dynamic System Measurement and Control, 1975, 97(3): 220.CrossRefGoogle Scholar
  5. 5.
    LIU Jian-chang, WANG Zhu. CMAC-Based Fuzzy Controller for Strip Flatness Pattern Recognition [J]. Journal of Northeastern University (Natural Science), 2005, 26(8): 718.zbMATHGoogle Scholar
  6. 6.
    Geraldo Souza, Reis Junior, Paulo E M Almeida. Modified Fuzzy-CMAC Networks With Clustering-Based Structure [A]. IEEE, eds. The 2006 IEEE International Joint Conference on Neural Networks [C]. Vancouver: IEEE, 2006. 2879.Google Scholar

Copyright information

© China Iron and Steel Research Institute Group 2008

Authors and Affiliations

  1. 1.College of Information Science and EngineeringYanshan UniversityQinhuangdao, HebeiChina

Personalised recommendations