On some stability problems of impulsive stochastic Cohen-Grossberg neural networks with mixed time delays

Abstract This paper covers the topic of both the p th moment ( p ⩾ 2 ) and almost sure stability of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays. We partially use a known result on exponential stability of impulsive stochastic functional differential systems, based on the Razumikhin type technique, and extend it to the case of stochastic neural networks using the Lyapunov function method and a Gronwall type inequality. Additionally, we consider the stability with respect to a general decay function which includes exponential, but also more general lower rate decay functions as the polynomial and the logarithmic ones. This fact gives us the opportunity to study general decay almost sure stability, even when the exponential one cannot be discussed. Suitable examples which support the theory are also presented.

[1]  John von Neumann,et al.  The Computer and the Brain , 1960 .

[2]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[3]  M. Arbib Brains, Machines, and Mathematics , 1987, Springer US.

[4]  Zheng Wu,et al.  Exponential stability of impulsive stochastic functional differential systems with Markovian switching , 2012, 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[5]  Zhiguo Yang,et al.  Further results on existence-uniqueness for stochastic functional differential equations , 2013 .

[6]  Biljana Tojtovska Stability Analysis of Impulsive Stochastic Cohen-Grossberg Neural Networks with Mixed Delays , 2012, ICT Innovations.

[7]  Qinghua Zhou,et al.  Exponential stability of stochastic delayed Hopfield neural networks , 2008, Appl. Math. Comput..

[8]  Xuerong Mao,et al.  RAZUMIKHIN-TYPE THEOREMS ON STABILITY OF STOCHASTIC NEURAL NETWORKS WITH DELAYS , 2001 .

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

[10]  J. Taylor Spontaneous behaviour in neural networks. , 1972, Journal of theoretical biology.

[11]  S. Jankovic,et al.  On the pth moment exponential stability criteria of neutral stochastic functional differential equations , 2007 .

[12]  X. Mao,et al.  Stochastic Differential Equations and Applications , 1998 .

[13]  Chengming Huang,et al.  Lasalle method and general decay stability of stochastic neural networks with mixed delays , 2012 .

[14]  Ping Chen,et al.  Dynamic Analysis of Stochastic Recurrent Neural Networks , 2008, Neural Processing Letters.

[16]  Jinde Cao,et al.  Stochastic Dynamics of Nonautonomous Cohen-Grossberg Neural Networks , 2011 .

[17]  Yonghui Sun,et al.  pth moment exponential stability of stochastic recurrent neural networks with time-varying delays , 2007 .

[18]  Tj Sejnowski,et al.  Skeleton filters in the brain , 2014 .

[19]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[20]  E. Zhu,et al.  Pth Moment Exponential Stability of Impulsive Stochastic Neural Networks with Mixed Delays , 2012 .

[21]  Zidong Wang,et al.  Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays , 2008 .

[22]  Xiaohu Wang,et al.  Exponential p-stability of impulsive stochastic Cohen-Grossberg neural networks with mixed delays , 2009, Math. Comput. Simul..

[23]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[24]  Svetlana Jankovic,et al.  Razumikhin-type theorems on general decay stability of stochastic functional differential equations with infinite delay , 2012, J. Comput. Appl. Math..