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Finite-Time Synchronization for Delayed Inertial Neural Networks by the Approach of the Same Structural Functions

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Abstract

This paper is concerned about the finite-time synchronization for the delayed drive-response inertial neural networks. Without applying the previous finite-time stability theorems, integral inequality way and the maximum-valued approach, by put forwarding a novel study approach: the way of the same structural functions, and devising the two kinds of novel controllers, two criteria to guarantee the finite-time synchronization for the networks are presented. The advantage of applying the same structural functions is that the computational complexity is greatly reduced in the proof of the main theorems. Our study presented in this paper are worthwhile in the study of FTS for neural networks and dynamical systems, and the approach and results obtained are sufficiently novel.

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Data Availibility Statement

All data included in this study are available upon request by contact with the corresponding author.

Abbreviations

FTS:

Finite-time synchronization

INNS:

Inertial neural networks

NN:

Neural network

GES:

Globally exponential synchronization

GAS:

Globally asymptotic synchronization

FTST:

Finite-time stability theory

IT:

Inequality techniques

NNS:

Neural networks

LFS:

Lyapunov functionals

LF:

Lyapunov functional

LST:

Lyapunov stability theory

FTAS:

Finite-time anti-synchronization

IIA:

Integral inequality approach

DR:

Drive-response

MVA:

Maximum-value approach

FITS:

Fixed-time synchronization

INN:

Inertial neural network

IVS:

Initial values

TSSF:

The same structural functions

TSSFA:

The same structural function approach

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Authors and Affiliations

  1. School of Mathematics and Information Science, Nanchang Normal University, Nanchang, 330032, China

    Huaying Liao

  2. College of Science, Hubei University of Technology, Wuhan, 430068, China

    Zhen Yang

  3. College of Mathematics, Hunan University, Changsha, 410082, China

    Zhengqiu Zhang & Yin Zhou

Authors
  1. Huaying Liao
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  2. Zhen Yang
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  3. Zhengqiu Zhang
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  4. Yin Zhou
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Correspondence to Zhengqiu Zhang.

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Fund:Science and technology project of Jiangxi education department(No:GJJ212607;No:GJJ191116; No:GJJ202602).

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Liao, H., Yang, Z., Zhang, Z. et al. Finite-Time Synchronization for Delayed Inertial Neural Networks by the Approach of the Same Structural Functions. Neural Process Lett 55, 4973–4988 (2023). https://doi.org/10.1007/s11063-022-11075-2

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