Modular Neural Network Rule Extraction Technique in Application to Country Stock Cooperate Governance Structure

  • Dang-Yong Du
  • Hai-Lin Lan
  • Wei-Xin Ling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Neural networks learn knowledge from data. For a monolithic structure, this knowledge can be easily used but not isolated. The many degrees of freedom while learning make ruler extraction a computationally intensive process as the representation is nor unique. Based on the technology of modular neural network data mining, this paper applied modular neural network ruler extraction to the data mining of country stock cooperate governance structure. Meanwhile, it investigated the relationship among gerentocratic constitutes of country stock cooperate, farmers’ educational level, labor force and corporation performance of country stock cooperate.


Hide Node Ruler Extraction Hide Layer Node Modular Neural Network Village Committee 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dang-Yong Du
    • 1
  • Hai-Lin Lan
    • 1
  • Wei-Xin Ling
    • 2
  1. 1.School of Business AdministrationSouth China University of TechnologyGuangzhouChina
  2. 2.School of Mathematical ScienceSouth China University of TechnologyGuangzhouChina

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