Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays. Academic Article

abstract

• This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Liu, H., Wang, Z., Shen, B. o., Huang, T., & Alsaadi, F. E.

citation count

• 62

complete list of authors

• Liu, Hongjian||Wang, Zidong||Shen, Bo||Huang, Tingwen||Alsaadi, Fuad E

publication date

• June 2018

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Computer Simulation
• Discrete-time Networks
• Exponential Stability
• Leakage Delays
• Memristive Neural Networks
• Neural Networks, Computer
• Probabilistic Time-varying Delays
• Stochastic Neural Networks
• Stochastic Processes
• Time Factors

PubMed Central ID

• 29510262

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2018.02.003

start page

• 1

end page

• 9

volume

• 102

URL

• http://dx.doi.org/10.1016/j.neunet.2018.02.003