Improved global robust exponential stability criteria for interval neural networks with time-varying delays

In this paper, by developing a new approach based on H-matrix theory, some improved sufficient conditions for global robust exponential stability of interval neural networks with time-varying delays are presented. Theoretic analysis shows that our results improve and generalize some previously published ones. Finally, some numerical examples are given to show the effectiveness of the obtained results.

[1]  Vimal Singh,et al.  Global robust stability of delayed neural networks: an LMI approach , 2005, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Jinde Cao,et al.  Global robust stability of delayed recurrent neural networks , 2004 .

[3]  Sabri Arik,et al.  New results for robust stability of dynamical neural networks with discrete time delays , 2010, Expert Syst. Appl..

[4]  Neyir Ozcan,et al.  Global robust stability analysis of neural networks with multiple time delays , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  Jiye Zhang.  Global exponential stability of interval neural networks with variable delays , 2006, Appl. Math. Lett..

[6]  Jinde Cao,et al.  Novel results concerning global robust stability of delayed neural networks , 2006 .

[7]  Jinde Cao,et al.  Global asymptotic and robust stability of recurrent neural networks with time delays , 2005, IEEE Trans. Circuits Syst. I Regul. Pap..

[8]  Houduo Qi.  New Sufficient Conditions for Global Robust Stability of Delayed Neural Networks , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  Charles R. Johnson,et al.  Topics in Matrix Analysis , 1991 .

[10]  Changyin Sun,et al.  Global Robust Exponential Stability of Interval Neural Networks with Delays , 2002, Neural Processing Letters.

[11]  Ting-Zhu Huang,et al.  A Note on “Global Robust Stability Criteria for Interval Delayed Neural Networks Via an LMI Approach” , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[12]  Sabri Arik,et al.  Novel results for global robust stability of delayed neural networks , 2009 .

[13]  Jinde Cao,et al.  Global robust stability of interval cellular neural networks with time-varying delays , 2005 .

[14]  Vimal Singh,et al.  Improved global robust stability criterion for delayed neural networks , 2007 .

[15]  S. Arik.  Global robust stability analysis of neural networks with discrete time delays , 2005 .

[16]  Vimal Singh,et al.  A new criterion for global robust stability of interval delayed neural networks , 2008 .

[17]  Huaguang Zhang,et al.  Robust Exponential Stability of Recurrent Neural Networks With Multiple Time-Varying Delays , 2007, IEEE Transactions on Circuits and Systems II: Express Briefs.

[18]  Yong Zhang,et al.  Improved Global Robust Stability Criteria for Delayed Neural Networks , 2007, IEEE Transactions on Circuits and Systems II: Express Briefs.

[19]  Jinde Cao,et al.  Globally exponentially robust stability and periodicity of delayed neural networks , 2004 .

[20]  Chuandong Li,et al.  Global Robust Stability Criteria for Interval Delayed Neural Networks Via an LMI Approach , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[21]  S. Arik,et al.  An analysis of global robust stability of neural networks with discrete time delays , 2006 .

[22]  Zhengzhi Han,et al.  Global stability analysis of interval neural networks with discrete and distributed delays of neutral type , 2009, Expert Syst. Appl..

[23]  Xiaofeng Liao,et al.  Robust stability for interval Hopfield neural networks with time delay , 1998, IEEE Trans. Neural Networks.

[24]  Weiwei Zhang,et al.  Global exponential stability of delayed reaction-diffusion neural networks with time-varying coefficients , 2009, Expert Syst. Appl..