Global dissipativity analysis for delayed quaternion-valued neural networks

Zhengwen Tu; Jinde Cao; Ahmed Alsaedi; Tasawar Hayat

doi:10.1016/j.neunet.2017.01.006

Global dissipativity analysis for delayed quaternion-valued neural networks

Zhengwen Tu
, Jinde Cao
, Ahmed Alsaedi
, Tasawar Hayat

Southeast University, Nanjing
Chongqing Three Gorges University
Faculty of Sciences, King Abdulaziz University
Quaid-I-Azam University

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

146 Scopus citations

Abstract

The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples.

Original language	English
Pages (from-to)	97-104
Number of pages	8
Journal	Neural Networks
Volume	89
DOIs	https://doi.org/10.1016/j.neunet.2017.01.006
State	Published - 1 May 2017
Externally published	Yes

Keywords

Delay
Dissipativity
Positive invariant sets
Quaternion-valued neural networks (QVNNs)

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10.1016/j.neunet.2017.01.006

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title = "Global dissipativity analysis for delayed quaternion-valued neural networks",

abstract = "The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples.",

keywords = "Delay, Dissipativity, Positive invariant sets, Quaternion-valued neural networks (QVNNs)",

author = "Zhengwen Tu and Jinde Cao and Ahmed Alsaedi and Tasawar Hayat",

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T1 - Global dissipativity analysis for delayed quaternion-valued neural networks

AU - Tu, Zhengwen

AU - Cao, Jinde

AU - Alsaedi, Ahmed

AU - Hayat, Tasawar

PY - 2017/5/1

Y1 - 2017/5/1

N2 - The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples.

AB - The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well. Finally, the effectiveness is notarized by deducing two simulation examples.

KW - Delay

KW - Dissipativity

KW - Positive invariant sets

KW - Quaternion-valued neural networks (QVNNs)

UR - https://www.scopus.com/pages/publications/85013213778

U2 - 10.1016/j.neunet.2017.01.006

DO - 10.1016/j.neunet.2017.01.006

M3 - Article

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AN - SCOPUS:85013213778

SN - 0893-6080

VL - 89

SP - 97

EP - 104

JO - Neural Networks

JF - Neural Networks

ER -

Global dissipativity analysis for delayed quaternion-valued neural networks

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Keywords

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