Quasi-bipartite synchronization of signed delayed neural networks under impulsive effects

This paper mainly studies quasi-bipartite synchronization (QBPS) of signed delayed neural networks (SDNNs) under impulsive effects, in which the nodes have cooperative as well as antagonistic interactions. It is assumed that disturbance occurs in the communication channels between some neighboring agents at impulsive occurring instants. Under the balanced network topology, some sufficient criteria to achieve QBPS of SDNNs are proposed by utilizing algebraic graph theory and extended Halanay differential inequality. Moreover, for the QBPS error of SDNNs, the upper bound of the final error state is also provided explicitly. Two numerical examples are presented to demonstrate the correctness of the theoretical results.

[1]  Tianping Chen,et al.  Synchronization of coupled connected neural networks with delays , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[2]  Jiangping Hu,et al.  Adaptive bipartite consensus on coopetition networks , 2015 .

[3]  Jinde Cao,et al.  Cluster synchronization in an array of hybrid coupled neural networks with delay , 2009, Neural Networks.

[4]  Sabri Arik,et al.  An analysis of exponential stability of delayed neural networks with time varying delays , 2004, Neural Networks.

[5]  Mingyue Zhai Estimation of Impulse Noise Parameters in Power Line Communications Channel Based on Artificial Neural Networks , 2006, 2006 8th international Conference on Signal Processing.

[6]  Xiaodi Li,et al.  Review of stability and stabilization for impulsive delayed systems. , 2018, Mathematical biosciences and engineering : MBE.

[7]  Qingshan Liu,et al.  A Projection Neural Network for Constrained Quadratic Minimax Optimization , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Xiao-lin Gong,et al.  Modeling the colored background noise of power line communication channel based on artificial neural network , 2010, The 19th Annual Wireless and Optical Communications Conference (WOCC 2010).

[9]  Zhenjiang Zhao,et al.  Global exponential stability of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays , 2016, Neural Networks.

[10]  Xiaodi Li,et al.  Persistent impulsive effects on stability of functional differential equations with finite or infinite delay , 2018, Appl. Math. Comput..

[11]  James Lam,et al.  Quasi-synchronization of heterogeneous dynamic networks via distributed impulsive control: Error estimation, optimization and design , 2015, Autom..

[12]  Jinde Cao,et al.  Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Huijun Gao,et al.  On Controllability of Neuronal Networks With Constraints on the Average of Control Gains , 2014, IEEE Transactions on Cybernetics.

[14]  Jianbin Qiu,et al.  Fuzzy Adaptive Finite-Time Fault-Tolerant Control for Strict-Feedback Nonlinear Systems , 2020, IEEE Transactions on Fuzzy Systems.

[15]  Fuad E. Alsaadi,et al.  Synchronization of dynamical networks with nonlinearly coupling function under hybrid pinning impulsive controllers , 2018, J. Frankl. Inst..

[16]  Daoyi Xu,et al.  Stability Analysis of Delay Neural Networks With Impulsive Effects , 2005, IEEE Trans. Circuits Syst. II Express Briefs.

[17]  Jinde Cao,et al.  Bipartite synchronization in coupled delayed neural networks under pinning control , 2018, Neural Networks.

[18]  Qingdu Li,et al.  Bipartite synchronization in a network of nonlinear systems: A contraction approach , 2016, J. Frankl. Inst..

[19]  Guanghui Wen,et al.  Bipartite synchronization of Lur'e network under signed digraph , 2018, International Journal of Robust and Nonlinear Control.

[20]  Bo Wu,et al.  A Halanay-type inequality approach to the stability analysis of discrete-time neural networks with delays , 2015, Appl. Math. Comput..

[21]  Hamid Reza Karimi,et al.  A Novel Finite-Time Control for Nonstrict Feedback Saturated Nonlinear Systems With Tracking Error Constraint , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Tingwen Huang,et al.  Persistence of delayed cooperative models: Impulsive control method , 2019, Appl. Math. Comput..

[23]  Yang Liu,et al.  Global stability of Clifford-valued recurrent neural networks with time delays , 2015, Nonlinear Dynamics.

[24]  Frank C. Hoppensteadt,et al.  Pattern recognition via synchronization in phase-locked loop neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[25]  Xiaodi Li,et al.  Complete Stability Analysis of Complex-Valued Neural Networks with Time Delays and Impulses , 2014, Neural Processing Letters.

[26]  Rathinasamy Sakthivel,et al.  Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation , 2018, Neural Networks.

[27]  Zhengjie Wang,et al.  Consensus disturbance rejection with channel uncertainties in directed leader-following network system , 2018, Cluster Computing.

[28]  James Lam,et al.  Global exponential stability of impulsive high-order BAM neural networks with time-varying delays , 2006, Neural Networks.

[29]  H. Antosiewicz,et al.  Differential Equations: Stability, Oscillations, Time Lags , 1967 .

[30]  Jianlong Qiu,et al.  Output tracking control of delayed switched systems via state-dependent switching and dynamic output feedback , 2019, Nonlinear Analysis: Hybrid Systems.

[31]  Martin Bohner,et al.  An impulsive delay differential inequality and applications , 2012, Comput. Math. Appl..

[32]  Guanghui Wen,et al.  Swarming Behavior of Multiple Euler–Lagrange Systems With Cooperation–Competition Interactions: An Auxiliary System Approach , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[33]  Jinde Cao,et al.  Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control , 2017, Neural Networks.

[34]  Jinde Cao,et al.  Pth Moment Exponential Stochastic Synchronization of Coupled Memristor-based Neural Networks with Mixed Delays via Delayed Impulsive Control , 2015, Neural Networks.

[35]  Sang-Moon Lee,et al.  Enhanced stability criteria of neural networks with time-varying delays via a generalized free-weighting matrix integral inequality , 2018, J. Frankl. Inst..

[36]  Fuad E. Alsaadi,et al.  Bipartite consensus for multi-agent systems with antagonistic interactions and communication delays , 2018 .

[37]  Jinde Cao,et al.  Stability Analysis of Quaternion-Valued Neural Networks: Decomposition and Direct Approaches , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Deyuan Meng,et al.  Bipartite containment tracking of signed networks , 2017, Autom..

[39]  Jianbin Qiu,et al.  Adaptive Fuzzy Control for Nontriangular Structural Stochastic Switched Nonlinear Systems With Full State Constraints , 2019, IEEE Transactions on Fuzzy Systems.

[40]  Feng Qian,et al.  Secure impulsive synchronization control of multi-agent systems under deception attacks , 2018, Inf. Sci..

[41]  Jinde Cao,et al.  Pinning Synchronization of Nonlinear Coupled Lur’e Networks Under Hybrid Impulses , 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.

[42]  Mingjun Du,et al.  Interval Bipartite Consensus of Networked Agents Associated With Signed Digraphs , 2016, IEEE Transactions on Automatic Control.

[43]  Maria Elena Valcher,et al.  On the consensus and bipartite consensus in high-order multi-agent dynamical systems with antagonistic interactions , 2014, Syst. Control. Lett..

[44]  Tianping Chen,et al.  Partial synchronization in linearly and symmetrically coupled ordinary differential systems , 2009 .

[45]  Jinde Cao,et al.  Exponential Synchronization of Linearly Coupled Neural Networks With Impulsive Disturbances , 2011, IEEE Transactions on Neural Networks.

[46]  Claudio Altafini,et al.  Consensus Problems on Networks With Antagonistic Interactions , 2013, IEEE Transactions on Automatic Control.

[47]  Jinde Cao,et al.  A unified synchronization criterion for impulsive dynamical networks , 2010, Autom..