Stability analysis of autonomous ratio-memory cellular nonlinear networks for pattern recognition

Su Yung Tsai*, Chi-Hsu Wang, Chung-Yu Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The stability analysis via the Lyapunov theorem for Autonomous Ratio-Memory Cellular Nonlinear Networks (ARMCNNs) is proposed. A conservative domain of attraction (DOA) is found from the stability analysis through a graphical method without complicated numerical analysis. The stability analysis shows that ARMCNNs can tolerate large ratio weight variations. This paper also presents the ARMCNN with self-feedback (SARMCNN) to overcome the problem of isolated neurons due to low correlation between neighboring neurons. The SARMCNN recognition rate (RR) is compared with other CNN constructed via the singular value decomposition technique (SVD-CNN).

Original languageEnglish
Article number5406043
Pages (from-to)2156-2167
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume57
Issue number8
DOIs
StatePublished - 9 Feb 2010

Keywords

  • Cellular nonlinear network (CNN)
  • Hebbian learning rule
  • Lyapunov stability
  • domain of attraction (DOA)
  • ratio memory (RM)

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