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Self-organized Path Constraint Neural Network: Structure and Algorithm

  • Hengqing Tong
  • Li Xiong
  • Hui Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

Abstract

Due to its flexibility and self-determination, self-organized learning neural network(NN) has been widely applied in many fields. Meanwhile, it has a well trend to develop. In our research, we find that structural equation modeling (SEM) may be reconstructed into a self-organized learning neural network, but the algorithm of NN need to be improved. In this paper, we first present an improved partial least square (PLS) algorithm in SEM using a suitable iterative initial value with constraint of unit vector. Then we propose a new self-organized path constraint neural network(SPCNN) based on SEM. Furthermore, we give the topology structure of SPCNN, describe the learning algorithm of SPCNN, including common algorithm and algorithm with a suitable initial weights value, and elaborate the function of SPCNN.

Keywords

Output Variable Partial Less Square Lower Triangular Matrix Neural Network Algorithm Partial Less Square 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hengqing Tong
    • 1
  • Li Xiong
    • 2
  • Hui Peng
    • 1
  1. 1.Department of MathematicsWuhan University of TechnologyHubeiP.R. China
  2. 2.School of ComputerWuhan University of TechnologyWuhan, HubeiP.R. China

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