Novel results on finite-time stabilization of state-based switched chaotic inertial neural networks with distributed delays

The p-norm finite-time stabilization (FTS) issue of a class of state-based switched inertial chaotic neural networks (SBSCINNs) with distributed time-varying delays is investigated. By using a suitable variable transformation, such second-order SBSCINNs are turned into the first-order differential equations. Then some novel criteria are obtained to stabilize SBSCINNs in a finite time based on the theory of finite-time control and non-smooth analysis together with designing two proper delay-dependent feedback controllers. Besides, the settling time of FTS is also estimated and discussed. Finally, the validity and practicability of the deduced theoretical results are verified by examples and applications.

[1]  Tingwen Huang,et al.  Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control , 2018, Neurocomputing.

[2]  Pagavathigounder Balasubramaniam,et al.  Synchronization of Markovian jumping inertial neural networks and its applications in image encryption , 2016, Neural Networks.

[3]  Sabri Arik,et al.  A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks , 2013, Neural Networks.

[4]  Zhigang Zeng,et al.  Memristor-based circuit implementation of pulse-coupled neural network with dynamical threshold generators , 2018, Neurocomputing.

[5]  Diek W. Wheeler,et al.  Stability and chaos in an inertial two-neuron system , 1997 .

[6]  Jinde Cao,et al.  Stability analysis of inertial Cohen-Grossberg neural networks with Markovian jumping parameters , 2017, Neurocomputing.

[7]  Adel M. Alimi,et al.  Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication , 2019, Neurocomputing.

[8]  Yunquan Ke,et al.  Anti-periodic Solutions of Inertial Neural Networks with Time Delays , 2016, Neural Processing Letters.

[9]  Guodong Zhang,et al.  Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Zhigang Zeng,et al.  Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays , 2018, Neural Networks.

[11]  Sabri Arik,et al.  Further analysis of global robust stability of neural networks with multiple time delays , 2012, J. Frankl. Inst..

[12]  Zhigang Zeng,et al.  New results on global exponential dissipativity analysis of memristive inertial neural networks with distributed time-varying delays , 2018, Neural Networks.

[13]  Jinde Cao,et al.  Novel global robust exponential stability criterion for uncertain inertial-type BAM neural networks with discrete and distributed time-varying delays via Lagrange sense , 2018, J. Frankl. Inst..

[14]  M. Syed Ali,et al.  Finite-time stability for memristor based switched neural networks with time-varying delays via average dwell time approach , 2018, Neurocomputing.

[15]  Zhiyong Yu,et al.  Global stability of complex-valued recurrent neural networks with both mixed time delays and impulsive effect , 2017, Neurocomputing.

[16]  Zhigang Zeng,et al.  GST-memristor-based online learning neural networks , 2018, Neurocomputing.

[17]  Zhigang Zeng,et al.  Anti-Synchronization of Delayed State-Based Switched Inertial Neural Networks , 2019, IEEE Transactions on Cybernetics.

[18]  Zhixia Ding,et al.  Finite time stabilization of delayed neural networks , 2015, Neural Networks.

[19]  Wei Zhang,et al.  Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses , 2017, Neural Networks.

[20]  Robert M. Westervelt,et al.  Stability and dynamics of simple electronic neural networks with added inertia , 1986 .

[21]  Aleksej F. Filippov,et al.  Differential Equations with Discontinuous Righthand Sides , 1988, Mathematics and Its Applications.

[22]  Frank L. Lewis,et al.  Exponential Stabilization of Fuzzy Memristive Neural Networks With Hybrid Unbounded Time-Varying Delays , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Lihong Huang,et al.  Finite-time stabilization control of memristor-based neural networks☆ , 2016 .

[24]  Jigui Jian,et al.  Exponential synchronization of inertial neural networks with mixed time-varying delays via periodically intermittent control , 2019, Neurocomputing.

[25]  Linlin Liu,et al.  Multistability of Switched Neural Networks With Piecewise Linear Activation Functions Under State-Dependent Switching , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[26]  Zhigang Zeng,et al.  Passivity and Passification of Fuzzy Memristive Inertial Neural Networks on Time Scales , 2018, IEEE Transactions on Fuzzy Systems.

[27]  Piyapong Niamsup,et al.  Novel criteria for finite-time stabilization and guaranteed cost control of delayed neural networks , 2015, Neurocomputing.

[28]  Yuming Feng,et al.  Exponential synchronization of inertial neural networks with mixed delays via quantized pinning control , 2018, Neurocomputing.

[29]  M. Forti,et al.  Generalized Lyapunov approach for convergence of neural networks with discontinuous or non-Lipschitz activations , 2006 .

[30]  Ling Wang,et al.  Bifurcation and Control in an Inertial Two-Neuron System with Time delays , 2012, Int. J. Bifurc. Chaos.

[31]  Jun Wang,et al.  Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[32]  Jinde Cao,et al.  Global dissipativity of memristor-based neutral type inertial neural networks , 2017, Neural Networks.

[33]  Guodong Zhang,et al.  Novel conditions on exponential stability of a class of delayed neural networks with state-dependent switching , 2015, Neural Networks.

[34]  Jun Tani,et al.  Model-based learning for mobile robot navigation from the dynamical systems perspective , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[35]  Quanbo Ge,et al.  Finite-Time Synchronization of Memristor-Based Recurrent Neural Networks With Inertial Items and Mixed Delays , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[36]  Yan Wang,et al.  A novel memristive Hopfield neural network with application in associative memory , 2017, Neurocomputing.

[37]  Leon O. Chua.  Resistance switching memories are memristors , 2011 .

[38]  Jinde Cao,et al.  A new switching design to finite-time stabilization of nonlinear systems with applications to neural networks , 2014, Neural Networks.

[39]  Zhigang Zeng,et al.  Lagrange Stability and Finite-Time Stabilization of Fuzzy Memristive Neural Networks With Hybrid Time-Varying Delays , 2020, IEEE Transactions on Cybernetics.

[40]  Chee Peng Lim,et al.  Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[41]  Zhigang Zeng,et al.  Finite-time stabilization of memristor-based inertial neural networks with discontinuous activations and distributed delays , 2019, J. Frankl. Inst..

[42]  Lixiang Li,et al.  Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks , 2018, Commun. Nonlinear Sci. Numer. Simul..

[43]  Sabri Arik,et al.  A new robust stability criterion for dynamical neural networks with multiple time delays , 2013, Neurocomputing.

[44]  Jingfeng Wang,et al.  Global Lagrange stability for inertial neural networks with mixed time-varying delays , 2017, Neurocomputing.

[45]  Zhidong Teng,et al.  Finite-time synchronization for memristor-based neural networks with time-varying delays , 2015, Neural Networks.