Stability analysis for delayed Cohen–Grossberg Clifford-valued neutral-type neural networks

Ramalingam Sriraman; Grienggrai Rajchakit; Oh Min Kwon; Sang Moon Lee

doi:10.1002/mma.8426

Stability analysis for delayed Cohen–Grossberg Clifford-valued neutral-type neural networks

Ramalingam Sriraman, Grienggrai Rajchakit, Oh Min Kwon, Sang Moon Lee

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13 Scopus citations

Abstract

The aim of this study is to explore the global stability of Cohen–Grossberg Clifford-valued neutral-type neural network models with time delays. In order to achieve the aim of this paper, and to solve the non-commutativity problem caused by Clifford numbers multiplication, the original Clifford-valued system is first decomposed into (Formula presented.) -dimensional real-valued systems. Some sufficient criteria for the global stability of the addressed network models are established by constructing an appropriate Lyapunov functional. The established stability conditions have not been affected by the neutral delay and time delay values. The proposed method and results of this paper are new. The feasibility of the stability criteria obtained are verified using two numerical examples.

Original language	English
Pages (from-to)	10925-10945
Number of pages	21
Journal	Mathematical Methods in the Applied Sciences
Volume	45
Issue number	17
DOIs	https://doi.org/10.1002/mma.8426
State	Published - 30 Nov 2022

Keywords

Clifford-valued neural network
Cohen–Grossberg neural network
Lyapunov functional
neutral delays
stability

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10.1002/mma.8426

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title = "Stability analysis for delayed Cohen–Grossberg Clifford-valued neutral-type neural networks",

abstract = "The aim of this study is to explore the global stability of Cohen–Grossberg Clifford-valued neutral-type neural network models with time delays. In order to achieve the aim of this paper, and to solve the non-commutativity problem caused by Clifford numbers multiplication, the original Clifford-valued system is first decomposed into (Formula presented.) -dimensional real-valued systems. Some sufficient criteria for the global stability of the addressed network models are established by constructing an appropriate Lyapunov functional. The established stability conditions have not been affected by the neutral delay and time delay values. The proposed method and results of this paper are new. The feasibility of the stability criteria obtained are verified using two numerical examples.",

keywords = "Clifford-valued neural network, Cohen–Grossberg neural network, Lyapunov functional, neutral delays, stability",

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T1 - Stability analysis for delayed Cohen–Grossberg Clifford-valued neutral-type neural networks

AU - Sriraman, Ramalingam

AU - Rajchakit, Grienggrai

AU - Kwon, Oh Min

AU - Lee, Sang Moon

PY - 2022/11/30

Y1 - 2022/11/30

N2 - The aim of this study is to explore the global stability of Cohen–Grossberg Clifford-valued neutral-type neural network models with time delays. In order to achieve the aim of this paper, and to solve the non-commutativity problem caused by Clifford numbers multiplication, the original Clifford-valued system is first decomposed into (Formula presented.) -dimensional real-valued systems. Some sufficient criteria for the global stability of the addressed network models are established by constructing an appropriate Lyapunov functional. The established stability conditions have not been affected by the neutral delay and time delay values. The proposed method and results of this paper are new. The feasibility of the stability criteria obtained are verified using two numerical examples.

AB - The aim of this study is to explore the global stability of Cohen–Grossberg Clifford-valued neutral-type neural network models with time delays. In order to achieve the aim of this paper, and to solve the non-commutativity problem caused by Clifford numbers multiplication, the original Clifford-valued system is first decomposed into (Formula presented.) -dimensional real-valued systems. Some sufficient criteria for the global stability of the addressed network models are established by constructing an appropriate Lyapunov functional. The established stability conditions have not been affected by the neutral delay and time delay values. The proposed method and results of this paper are new. The feasibility of the stability criteria obtained are verified using two numerical examples.

KW - Clifford-valued neural network

KW - Cohen–Grossberg neural network

KW - Lyapunov functional

KW - neutral delays

KW - stability

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JO - Mathematical Methods in the Applied Sciences

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Stability analysis for delayed Cohen–Grossberg Clifford-valued neutral-type neural networks

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