Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Probabilistic Time-Varying Delays

Meng Hui; Jiahuang Zhang; Jiao Zhang; Herbert Ho Ching Iu; Rui Yao; Lin Bai

doi:10.1109/ACCESS.2021.3066585

Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Probabilistic Time-Varying Delays

Meng Hui, Jiahuang Zhang, Jiao Zhang, Herbert Ho Ching Iu, Rui Yao, Lin Bai

Electrical, Electronic and Computer Engineering

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

2 Citations (Scopus)

Abstract

This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results.

Original language	English
Article number	9380365
Pages (from-to)	44784-44796
Number of pages	13
Journal	IEEE Access
Volume	9
DOIs	https://doi.org/10.1109/ACCESS.2021.3066585
Publication status	Published - 2021

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title = "Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Probabilistic Time-Varying Delays",

abstract = "This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results.",

keywords = "finite-time, probabilistic time-varying delays, Projective synchronization, stochastic complex-valued neural networks",

author = "Meng Hui and Jiahuang Zhang and Jiao Zhang and Iu, {Herbert Ho Ching} and Rui Yao and Lin Bai",

year = "2021",

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T1 - Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Probabilistic Time-Varying Delays

AU - Hui, Meng

AU - Zhang, Jiahuang

AU - Zhang, Jiao

AU - Iu, Herbert Ho Ching

AU - Yao, Rui

AU - Bai, Lin

PY - 2021

Y1 - 2021

N2 - This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results.

AB - This paper addresses finite-time projective synchronization of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays (PTVs). First, in the complex domain, PTVs are introduced into the studied model and a novel feedback control scheme is constructed. Next, based on inequalities techniques and the Lyapunov stability approach, some novel projective synchronization criteria are established by decomposing SCVNNs into two equivalent real-valued systems. Moreover, a setting time function is created by employing lemma 4. Compared with previous researches, our theory content is an extension and complement to known results. Finally, numerical simulation is presented to validate the effectiveness of theoretical analysis results.

KW - finite-time

KW - probabilistic time-varying delays

KW - Projective synchronization

KW - stochastic complex-valued neural networks

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JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

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Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Probabilistic Time-Varying Delays

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