Effects of mixed time delays and D operators on fixed-time synchronization of discontinuous neutral-type neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lin Sun ◽  
Fanchao Kong ◽  
Hongjun Qiu ◽  
Yanhong Zhang
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Neutral Type ◽  
Fixed Time ◽  
Feedback Controllers ◽  
Control Laws ◽  
Mixed Time Delays ◽  
Inequality Technique ◽  
Error System ◽  
Functional Differential

Abstract In this paper, the fixed-time synchronization analysis is addressed for a class of discontinuous neutral-type neural networks. The focus is mainly on the design of useful control laws such that the constructed error system converges to zero in a fixed time. The major difficulty is to cope with the discontinuous neuron activations, D operators, time-varying discrete, and distributed delays simultaneously. To accomplish the target, a new and effective framework is firstly established. By means of functional differential inclusions theory, inequality technique and Lyapunov–Krasovskii functional, novel discontinuous feedback controllers are designed and some new verifiable algebraic criteria are derived to design the control gains. In contrast to the existed results on the neutral-type neural networks, the theoretical results of this paper are more general and rigorous. Finally, numerical examples and simulations are presented to illustrate the correctness of the main results.

scholarly journals Finite-/fixed-time synchronization of delayed Clifford-valued recurrent neural networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
N. Boonsatit ◽  
G. Rajchakit ◽  
R. Sriraman ◽  
C. P. Lim ◽  
P. Agarwal
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Computational Techniques ◽  
Feedback Controllers ◽  
System Models ◽  
Response System ◽  
Synchronization Problem ◽  
Error System

AbstractThis paper investigates the problem of finite-/fixed-time synchronization for Clifford-valued recurrent neural networks with time-varying delays. The considered Clifford-valued drive and response system models are firstly decomposed into real-valued drive and response system models in order to overcome the difficulty of the noncommutativity of the multiplication of Clifford numbers. Then, suitable time-delayed feedback controllers are devised to investigate the synchronization problem in finite-/fixed-time of error system. On the basis of new Lyapunov–Krasovskii functional and new computational techniques, finite-/fixed-time synchronization criteria are formulated for the corresponding real-valued drive and response system models. Two numerical examples demonstrate the effectiveness of the theoretical results.

scholarly journals Robust fixed-time synchronization of discontinuous Cohen–Grossberg neural networks with mixed time delays

2019 ◽  
Vol 24 (4) ◽  
Author(s):  
Fanchao Kong ◽  
Quanxin Zhu ◽  
Feng Liang ◽  
Juan J. Nieto
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Time Synchronization ◽  
Distributed Delay ◽  
Fixed Time ◽  
Control Procedure ◽  
Mixed Time Delays ◽  
Analysis Theory ◽  
Response Systems ◽  
Functional Differential

This paper aims to investigate the fixed-time synchronization (i.e., synchronization in fixed-time sense) of Cohen–Grossberg drive-response neural networks with discontinuous neuron activations and mixed time delays (both time-varying discrete delay and distributed delay). To accomplish the target of fixed-time synchronization, a novel discontinuous feedback control procedure is firstly designed for the response neural networks. Then, under the framework of Filippov solutions, by means of functional differential inclusions theory, inequality technique and the nonsmooth analysis theory with Lyapunov-like approach, some sufficient criteria are derived to design the control parameters for achieving fixed-time synchronization of the proposed drive-response systems. Finally, two numerical examples are presented to illustrate the proposed methodologies.

scholarly journals Fixed-Time Synchronization of Memristive Fuzzy BAM Cellular Neural Networks With Time-Varying Delays Based on Feedback Controllers

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 12085-12102 ◽  
Author(s):  
Mingwen Zheng ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jinghua Xiao ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Synchronization ◽  
Fixed Time ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Feedback Controllers

scholarly journals Finite-time synchronization for chaotic neural networks with stochastic disturbances

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Xuejun Shi ◽  
Yongshun Zhao ◽  
Xiaodi Li
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Basic Result ◽  
Fixed Time ◽  
System Response ◽  
Stochastic Disturbances ◽  
Chaotic Neural Networks ◽  
Response System ◽  
Error System

AbstractIn this paper, we focus on the problem of synchronization for chaotic neural networks with stochastic disturbances. Firstly, we provide a basic result that the systems including the drive system, response system, and error system have a unique solution on the whole time horizon. Based on this result, we design a new control law such that the response system can be synchronized with the drive chaotic system in finite time. Furthermore, we show that the settling time is independent of the initial data under some proper conditions, which hints that the fixed-time synchronization of chaotic neural networks can be realized by our proposed method. Finally, we give simulations to verify the theoretical analysis for our main results.

scholarly journals Finite time Synchronization of Inertial Memristive Neural Networks with Time Varying Delay

2021 ◽  
Vol 16 (4) ◽  
Author(s):  
Yajing Pang ◽  
Shengmei Dong
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Fixed Time ◽  
Memristive Neural Networks ◽  
Time Varying Delay ◽  
Error System ◽  
Varying Delay

Finite time synchronization control of inertial memristor-based neural networks with varying delay is considered. In view of drive and response concept, the sufficient conditions to ensure finite time synchronization issue of inertial memristive neural networks is given. Based on Lyapunov finite time asymptotic theory, a kind of feedback controllers is designed for inertial memristorbased neural networks to realize the finite time synchronization. Based on Lyapunov stability theory, close loop error system can be proved finite time and fixed time stable. Finally, illustrative example is given to illustrate the effectiveness of theoretical results.

scholarly journals Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jinde Cao ◽  
Abdulaziz Alofi ◽  
Abdullah Al-Mazrooei ◽  
Ahmed Elaiw
Keyword(s):  
Neural Networks ◽  
Matrix Measure ◽  
Chaotic Neural Networks ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Arbitrary Switching ◽  
Parameters Uncertainty ◽  
Inequality Technique ◽  
Error System ◽  
Switched Interval Networks

This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

Sign in / Sign up

Export Citation Format

Share Document