Delay-Dependent Passivity and Stability Analysis for a Class of Memristor-Based Neural Networks with Time Delay in the Leakage Term

This paper is concerned with the problem of passivity and stability analysis for a class of memristor-based neural networks with leakage delay and state-dependent switched memductance functions. By combining differential inclusions with set-valued maps and constructing a proper Lyapunov–Krasovskii functional, delay-dependent criteria in terms of linear matrix inequalities are obtained for the passivity of the memristive neural networks. Meanwhile, based on the derived criteria, stability criteria are obtained for the networks via Barbalat’s lemma. Finally, a numerical example is given to illustrate the feasibility of the theoretical results.

[1]  Chun-Guang Li,et al.  Passivity Analysis of Neural Networks With Time Delay , 2005, IEEE Trans. Circuits Syst. II Express Briefs.

[2]  Lixiang Li,et al.  Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations , 2014, Neural Processing Letters.

[3]  D. D. Perlmutter,et al.  Stability of time‐delay systems , 1972 .

[4]  K. C. Cheung,et al.  Passivity criteria for continuous-time neural networks with mixed time-varying delays , 2012, Appl. Math. Comput..

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

[6]  P. Balasubramaniam,et al.  Passivity analysis of neural networks with Markovian jumping parameters and interval time-varying delays ☆ , 2010 .

[7]  Shouchuan Hu Differential equations with discontinuous right-hand sides☆ , 1991 .

[8]  Yu Wang,et al.  Exponential passivity of memristive neural networks with mixed time-varying delays , 2016, J. Frankl. Inst..

[9]  Zhigang Zeng,et al.  Circuit design and exponential stabilization of memristive neural networks , 2015, Neural Networks.

[10]  Zhigang Zeng,et al.  Passivity analysis of memristive neural networks with different memductance functions , 2014, Commun. Nonlinear Sci. Numer. Simul..

[11]  James Lam,et al.  New passivity criteria for neural networks with time-varying delay , 2009, Neural Networks.

[12]  Guodong Zhang,et al.  Global anti-synchronization of a class of chaotic memristive neural networks with time-varying delays , 2013, Neural Networks.

[13]  Lixiang Li,et al.  Synchronization control of memristor-based recurrent neural networks with perturbations , 2014, Neural Networks.

[14]  P. Balasubramaniam,et al.  Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays , 2010 .

[15]  Zhigang Zeng,et al.  Exponential Stabilization of Memristive Neural Networks With Time Delays , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Jinde Cao,et al.  Passivity and Passification of Memristor-Based Recurrent Neural Networks With Additive Time-Varying Delays , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Shouming Zhong,et al.  New passivity criteria for memristive uncertain neural networks with leakage and time-varying delays. , 2015, ISA transactions.

[18]  Zhigang Zeng,et al.  Noise cancellation of memristive neural networks , 2014, Neural Networks.

[19]  Song Zhu,et al.  Passivity analysis of stochastic delayed neural networks with Markovian switching , 2011, Neurocomputing.

[20]  Wei Xing Zheng,et al.  Passivity-based sliding mode control of uncertain singular time-delay systems , 2009, Autom..

[21]  Qingling Zhang,et al.  Delay-dependent passivity criterion for discrete-time delayed standard neural network model , 2010, Neurocomputing.

[22]  James Lam,et al.  Exponential estimates and stabilization of uncertain singular systems with discrete and distributed delays , 2008, Int. J. Control.

[23]  Tzann-Shin Lee,et al.  Lagrangian modeling and passivity-based control of three-phase AC/DC voltage-source converters , 2004, IEEE Trans. Ind. Electron..

[24]  K. Gopalsamy Stability and Oscillations in Delay Differential Equations of Population Dynamics , 1992 .

[25]  Ju H. Park,et al.  State estimation of memristor-based recurrent neural networks with time-varying delays based on passivity theory , 2014, Complex..

[26]  Lixian Zhang,et al.  Passivity and passification for Markov jump genetic regulatory networks with time-varying delays , 2014, Neurocomputing.

[27]  Pagavathigounder Balasubramaniam,et al.  A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay , 2011, Neurocomputing.

[28]  Zhigang Zeng,et al.  An improved criterion for stability and attractability of memristive neural networks with time-varying delays , 2014, Neurocomputing.

[29]  Jinde Cao,et al.  Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term , 2016, Appl. Math. Comput..

[30]  Zhigang Zeng,et al.  Dynamic analysis of memristive neural system with unbounded time-varying delays , 2014, J. Frankl. Inst..

[31]  Zdzisław Denkowski,et al.  Set-Valued Analysis , 2021 .

[32]  Pagavathigounder Balasubramaniam,et al.  Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays , 2012, Expert Syst. Appl..

[33]  Zhigang Zeng,et al.  Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays , 2013, Neural Networks.

[34]  Yanjun Shen,et al.  Finite-time synchronization control of a class of memristor-based recurrent neural networks , 2015, Neural Networks.

[35]  L. Chua Memristor-The missing circuit element , 1971 .

[36]  Frank Z. Wang,et al.  Delayed switching applied to memristor neural networks , 2012 .

[37]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[38]  Song Zhu,et al.  Exponential passivity of neural networks with time-varying delay and uncertainty , 2010 .

[39]  Zhigang Zeng,et al.  Exponential passivity of memristive neural networks with time delays , 2014, Neural Networks.