Convergence analysis of Chauvin’s PCA learning algorithm with a constant learning rate

Jian Cheng Lv and Zhang Yi

Chaos, Solitons & Fractals, 2007, vol. 32, issue 4, 1562-1571

Abstract: The convergence of Chauvin’s PCA learning algorithm with a constant learning rate is studied in this paper by using a DDT method (deterministic discrete-time system method). Different from the DCT method (deterministic continuous-time system method), the DDT method does not require that the learning rate converges to zero. An invariant set of Chauvin’s algorithm with a constant learning rate is obtained so that the non-divergence of this algorithm can be guaranteed. Rigorous mathematic proofs are provided to prove the local convergence of this algorithm.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:32:y:2007:i:4:p:1562-1571

DOI: 10.1016/j.chaos.2005.12.007

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