Periodicity of Cohen-Grossberg-type fuzzy neural networks with impulses and time-varying delays

Abstract The periodicity problem of a class of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays is concerned in this paper. Via constructing a delay differential inequality, and applying fuzzy theory and the Lyapunov method, several criteria which ensure the existence and exponential stability of the periodic solutions for the considered systems are derived. An example is shown to illustrate the validity of the obtained results.

[1]  Fuad E. Alsaadi,et al.  Recursive Distributed Filtering for a Class of State-Saturated Systems With Fading Measurements and Quantization Effects , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Ta-lun Yang,et al.  The global stability of fuzzy cellular neural network , 1996 .

[3]  Zhen Wang,et al.  Nonlinear dynamics and chaos in a simplified memristor-based fractional-order neural network with discontinuous memductance function , 2018, Nonlinear Dynamics.

[4]  Qing-Long Han,et al.  Envelope-constrained H∞ filtering for nonlinear systems with quantization effects: The finite horizon case , 2018, Autom..

[5]  Lei Zou,et al.  Recursive Filtering for Time-Varying Systems With Random Access Protocol , 2019, IEEE Transactions on Automatic Control.

[6]  Jinde Cao,et al.  Stability and periodicity in delayed cellular neural networks with impulsive effects , 2007 .

[7]  Zhidong Teng,et al.  Stability and periodicity in high-order neural networks with impulsive effects , 2008 .

[8]  Fuad E. Alsaadi,et al.  A new approach to non-fragile state estimation for continuous neural networks with time-delays , 2016, Neurocomputing.

[9]  Zhen Wang,et al.  Improved quasi-synchronization criteria for delayed fractional-order memristor-based neural networks via linear feedback control , 2018, Neurocomputing.

[10]  Zidong Wang,et al.  Event-Triggered $H_\infty$ State Estimation for Delayed Stochastic Memristive Neural Networks With Missing Measurements: The Discrete Time Case , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Pagavathigounder Balasubramaniam,et al.  Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays , 2014, International Journal of Machine Learning and Cybernetics.

[12]  Changjin Xu,et al.  Global exponential periodicity for fuzzy cellular neural networks with proportional delays , 2017, J. Intell. Fuzzy Syst..

[13]  Mei-Yan Lin,et al.  Novel stability conditions of fuzzy neural networks with mixed delays under impulsive perturbations , 2017 .

[14]  Chuandong Li,et al.  Periodicity and stability for variable-time impulsive neural networks , 2017, Neural Networks.

[15]  Qing-Long Han,et al.  Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements , 2018, IEEE Transactions on Cybernetics.

[16]  Liqun Zhou,et al.  Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays. , 2016, ISA transactions.

[17]  Yuxia Li,et al.  Stability and Hopf Bifurcation of Fractional-Order Complex-Valued Single Neuron Model with Time Delay , 2017, Int. J. Bifurc. Chaos.

[18]  Kelin Li,et al.  Impulsive effect on global exponential stability of BAM fuzzy cellular neural networks with time-varying delays , 2010, Int. J. Syst. Sci..

[19]  Di Zhao,et al.  $\mathcal{H}_{\infty}$ PID Control With Fading Measurements: The Output-Feedback Case , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[20]  Min Wu,et al.  State Estimation for Discrete Time-Delayed Genetic Regulatory Networks With Stochastic Noises Under the Round-Robin Protocols , 2018, IEEE Transactions on NanoBioscience.

[21]  Kelin Li,et al.  Global Exponential Stability of impulsive Fuzzy Cellular Neural Networks with Delays and Diffusion , 2009, Int. J. Bifurc. Chaos.

[22]  Fuad E. Alsaadi,et al.  Finite-Time State Estimation for Recurrent Delayed Neural Networks With Component-Based Event-Triggering Protocol , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Fuad E. Alsaadi,et al.  Non-fragile state estimation for discrete Markovian jumping neural networks , 2016, Neurocomputing.

[24]  Jinde Cao,et al.  Dynamical behaviors of discrete-time fuzzy cellular neural networks with variable delays and impulses , 2008, J. Frankl. Inst..

[25]  Qing-Long Han,et al.  Security Control for Discrete-Time Stochastic Nonlinear Systems Subject to Deception Attacks , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[26]  Jinde Cao,et al.  Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Lihong Huang,et al.  Existence and exponential stability of periodic solutions for a class of Cohen-Grossberg neural networks with time-varying delays , 2007 .

[28]  Kelin Li,et al.  Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms , 2009 .

[29]  Tingwen Huang.  Exponential stability of fuzzy cellular neural networks with distributed delay , 2006 .

[30]  Zidong Wang,et al.  Guaranteed cost control for uncertain nonlinear systems with mixed time-delays: The discrete-time case , 2018, Eur. J. Control.

[31]  Junwei Lu,et al.  Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays , 2018, Appl. Math. Comput..

[32]  Huaguang Zhang,et al.  Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays , 2009, Neurocomputing.

[33]  Jun Wang,et al.  Global exponential periodicity and global exponential stability of a class of recurrent neural networks with various activation functions and time-varying delays , 2007, Neural Networks.

[34]  Qing-Long Han,et al.  Finite-Time $H_{\infty}$ State Estimation for Discrete Time-Delayed Genetic Regulatory Networks Under Stochastic Communication Protocols , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[35]  Jia Jia,et al.  Quasi-synchronisation of fractional-order memristor-based neural networks with parameter mismatches , 2017 .