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)


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.


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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  1. 1.
    Grossberg, S.: Adaptive pattern classification and universal recoding: A Parallel development and coding of neural feature detectors. Biological Cybernetics 23, 121–134 (1976)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Malsburg, V.C.: Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100 (1973)CrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Self-Organization and Associative Memory, 2nd edn. Springer, Heidelberg (1987)Google Scholar
  4. 4.
    Tomarken, J.A., Waller, G.N.: Structural equation modeling: strengths, limitations and misconceptions. Annual Review of Clinic Psychology 1, 31–65 (2005)CrossRefGoogle Scholar
  5. 5.
    Wang, J.D., Chen, J.Z., Yang, Y.R.: Micronization of titanocene dichloride by rapid expansion of supercritical solution and its ethylene polymerization. The Journal of Supercritical Fluids 33(2), 159–172 (2005)CrossRefGoogle Scholar
  6. 6.
    Yum, L.S., Lu, B.: Case-deletion diagnostics for nonlinear structural equation models. Multivariate Behavioral Research 38(3), 375–400 (2003)CrossRefGoogle Scholar
  7. 7.
    Yum, L.S., Tu, Z.H.: Muximum likelihood estimation of nonlinear structural equation models. Psychometrika 67(2), 189–210 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Liang, J., Bentler, P.M.: An EM algorithm for fitting two-level structural equation models. Psychometrika 69, 101–122 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Yen, C.J., Konold, R.T., McDermott, A.P.: Does learning behavior augment cognitive ability as an indicator of academic achievement? Journal of School Psychology 42(2), 157–169 (2004)CrossRefGoogle Scholar
  10. 10.
    Fornell, C., Johnson, D.M., Anderson, W.E.: The American customer satisfaction index: nature, purpose, and findings. Journal of Marketing 60, 7–18 (1996)CrossRefGoogle Scholar
  11. 11.
    Introduction to Chinese CSI. Standards Press of China, Beijing. Centre for Enterprise Research of China, Tsinghua University, et al (2003)Google Scholar
  12. 12.
    Tenenhaus, M., et al.: PLS path modeling. Computational Statistics & Data Analysis 48(1), 159–205 (2005)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Bastien, P., Vinzi, E.V., Tenenhaus, M.: PLS generalised linear regression. Computational Statistics & Data Analysis 48(1), 17–46 (2005)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Tong, H.Q.: Data analysis & statistical computation (DASC) software. Science Press, Beijing (2005)Google Scholar
  15. 15.
    Shi, Z.Z.: Knowledge discovering. Tsinghua University, et al (2003)Google Scholar

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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