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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2001 / Cilt: 1 - Sayı: 2

Convergence of nonsymmetric discrete-time cellular neural networks

Sabri ARIK
Pages
223–225
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

In this paper, a sufficient condition is presented for the convergence of nonsymmetric dicret-time cellular neural networks DTCNNs, which generalises the previous convergence results dericed in the literature on the complete stability of DTCNNs.

📌 3 Key Takeaways

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Objective & Hypothesis:In this paper, a sufficient condition is presented for the convergence of nonsymmetric dicret-time cellular neural networks DTCNNs, which generalises the previous convergence results dericed in the literature on the complete stability of DTCNNs.
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Key Findings & Data:In this paper, a sufficient condition is presented for the convergence of nonsymmetric dicret-time cellular neural networks DTCNNs, which generalises the previous convergence results dericed in the literature on the complete stability of DTCNNs.
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Conclusion & Impact:In this paper, a sufficient condition is presented for the convergence of nonsymmetric dicret-time cellular neural networks DTCNNs, which generalises the previous convergence results dericed in the literature on the complete stability of DTCNNs.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this paper, a sufficient condition is presented for the convergence of nonsymmetric dicret-time cellular neural networks DTCNNs, which generalises the previous convergence results dericed in the literature on the complete stability of DTCNNs.

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Cite This Article

ARIK, S. (2022). Convergence of nonsymmetric discrete-time cellular neural networks. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 1 - Sayı: 2, 223–225. https://ajindex.com/articles/543970

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References (5)

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