ℋ∞Stability analysis for delayed Hopfield neural networks

C. K. Ahn

doi:10.1243/09596518JSCE870

ℋ_∞Stability analysis for delayed Hopfield neural networks

C. K. Ahn

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In this paper, an ℋ_∞ approach is used to derive a tuning algorithm for delayed Hopfield neural networks. Based on the Lyapunov stability theory, the ℋ_∞ learning law is presented to not only guarantee asymptotical stability but also reduce the effect of an external disturbance to an ℋ_∞ norm constraint. An existence condition for the proposed learning law is represented in terms of a linear matrix inequality (LMI). An illustrative example is provided to demonstrate the effectiveness of the proposed learning law.

Original language	English
Pages (from-to)	203-208
Number of pages	6
Journal	Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume	224
Issue number	2
DOIs	https://doi.org/10.1243/09596518JSCE870
Publication status	Published - 2010 Mar 1
Externally published	Yes

Keywords

Hopfield neural networks
Linear matrix inequality (LMI)
Lyapunov stability theory
Weight learning
ℋ approach

ASJC Scopus subject areas

Control and Systems Engineering
Mechanical Engineering

Access to Document

10.1243/09596518JSCE870

Cite this

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abstract = "In this paper, an ℋ∞ approach is used to derive a tuning algorithm for delayed Hopfield neural networks. Based on the Lyapunov stability theory, the ℋ∞ learning law is presented to not only guarantee asymptotical stability but also reduce the effect of an external disturbance to an ℋ∞ norm constraint. An existence condition for the proposed learning law is represented in terms of a linear matrix inequality (LMI). An illustrative example is provided to demonstrate the effectiveness of the proposed learning law.",

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AB - In this paper, an ℋ∞ approach is used to derive a tuning algorithm for delayed Hopfield neural networks. Based on the Lyapunov stability theory, the ℋ∞ learning law is presented to not only guarantee asymptotical stability but also reduce the effect of an external disturbance to an ℋ∞ norm constraint. An existence condition for the proposed learning law is represented in terms of a linear matrix inequality (LMI). An illustrative example is provided to demonstrate the effectiveness of the proposed learning law.

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