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Volume 7 Issue 6
Oct.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Haidi Dong, Yingbin Gao and Gang Liu, "Convergence Analysis of a Self-Stabilizing Algorithm for Minor Component Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1585-1592, Nov. 2020. doi: 10.1109/JAS.2019.1911636
Citation: Haidi Dong, Yingbin Gao and Gang Liu, "Convergence Analysis of a Self-Stabilizing Algorithm for Minor Component Analysis," IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1585-1592, Nov. 2020. doi: 10.1109/JAS.2019.1911636

Convergence Analysis of a Self-Stabilizing Algorithm for Minor Component Analysis

doi: 10.1109/JAS.2019.1911636
Funds:  This work was supported by the National Natural Science Foundation of China (61903375, 61673387, 61374120) and Shaanxi Province Natural Science Foundation (2016JM6015)
More Information
  • The Möller algorithm is a self-stabilizing minor component analysis algorithm. This research document involves the study of the convergence and dynamic characteristics of the Möller algorithm using the deterministic discrete time (DDT) methodology. Unlike other analysis methodologies, the DDT methodology is capable of serving the distinct time characteristic and having no constraint conditions. Through analyzing the dynamic characteristics of the weight vector, several convergence conditions are drawn, which are beneficial for its application. The performing computer simulations and real applications demonstrate the correctness of the analysis’s conclusions.

     

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    Highlights

    • When the smallest eigenvalues are equal, the dynamic characteristic analysis of M?ller algorithm is finished.
    • Some conditions are drawn to guarantee the convergence of the Möller algorithm.
    • The range of the learning rate is expanded.

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