Stochastic robust stability for neutral-type impulsive interval neural networks with distributed time-varying delays

[1]  Jitao Sun,et al.  Stability analysis of a class of stochastic differential delay equations with nonlinear impulsive effects , 2010, J. Frankl. Inst..

[2]  Huaguang Zhang,et al.  Stochastic stability analysis of neutral-type impulsive neural networks with mixed time-varying delays and Markovian jumping , 2010, Neurocomputing.

[3]  Kai Zhang,et al.  Stability of switched Hopfield neural networks with time-varying delay , 2010, Proceedings of the 2010 American Control Conference.

[4]  Rajcho Ilarionov,et al.  On global exponential stability for impulsive cellular neural networks with time-varying delays , 2010, Comput. Math. Appl..

[5]  Xiaodi Li,et al.  Global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type , 2010, Appl. Math. Comput..

[6]  Ivanka M. Stamova,et al.  Stability Analysis of Impulsive Functional Differential Equations , 2009 .

[7]  Pagavathigounder Balasubramaniam,et al.  Delay-interval dependent robust stability criteria for stochastic neural networks with linear fractional uncertainties , 2009, Neurocomputing.

[8]  Wu Min,et al.  Delay-dependent Robust Stability for Uncertain Stochastic Systems with Interval Time-varying Delay , 2009 .

[9]  Xuerong Mao,et al.  Delay-Dependent Exponential Stability of Neutral Stochastic Delay Systems , 2009, IEEE Transactions on Automatic Control.

[10]  Bin Liu,et al.  Stability of Solutions for Stochastic Impulsive Systems via Comparison Approach , 2008, IEEE Transactions on Automatic Control.

[11]  Li Xiaolin,et al.  Exponential robust stability of stochastic interval Hopfield neural networks with time-varying delays , 2008, 2008 27th Chinese Control Conference.

[12]  Gang Feng,et al.  Reliable dissipative control for stochastic impulsive systems , 2008, Autom..

[13]  Zhang Qunli,et al.  Impulsive Synchronization of Typical Hopfield Neural Networks , 2006, 2007 Chinese Control Conference.

[14]  Xiaofeng Liao,et al.  Global robust asymptotical stability of multi-delayed interval neural networks: an LMI approach , 2004 .

[15]  Mauro Forti,et al.  On global stability of Hopfield neural networks with discontinuous neuron activations , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[16]  K. Gu.  An integral inequality in the stability problem of time-delay systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[17]  Jyh-Ching Juang,et al.  Stability analysis of Hopfield-type neural networks , 1999, IEEE Trans. Neural Networks.

[18]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[19]  Edgar N. Sánchez,et al.  Input-to-state stability (ISS) analysis for dynamic neural networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[20]  Amir F. Atiya,et al.  How delays affect neural dynamics and learning , 1994, IEEE Trans. Neural Networks.

[21]  Masahiko Morita,et al.  Capacity of associative memory using a nonmonotonic neuron model , 1993, Neural Networks.

[22]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[23]  B. Øksendal.  Stochastic differential equations : an introduction with applications , 1987 .

[24]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Cheng Si-we,et al.  Stochastic stability analysis of fuzzy Hopfield neural networks with time-varying delays , 2008 .

[26]  E. Yaz.  Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[27]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[28]  M. Morita.  Original Contribution: Associative memory with nonmonotone dynamics , 1993 .

[29]  D. Baĭnov,et al.  Systems with impulse effect : stability, theory, and applications , 1989 .

[30]  J. Hopfield.  Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .