Global Exponential Stability and Synchronization for Discrete-Time Inertial Neural Networks With Time Delays: A Timescale Approach.

abstract

• This paper considers generalized discrete-time inertial neural network (GDINN). By timescale theory, the original network is rewritten as a timescale-type inertial NN. Two different scenarios are considered. In a first scenario, several criteria guaranteeing the global exponential stability for the addressed GDINN are obtained based on the generalized matrix measure concept. In this case, Lyapunov function or functional is not necessary. In a second scenario, some inequality analytical and scaling techniques are used to achieve the global exponential stability for the considered GDINN. The obtained criteria are also applied to the global exponential synchronization of drive-response GDINNs. Several illustrative examples, including applications to the pseudorandom number generator and encrypted image transmission, are given to show the effectiveness of the theoretical results.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

altmetric score

• 0.25

author list (cited authors)

• Xiao, Q., Huang, T., & Zeng, Z.

citation count

• 61

complete list of authors

• Xiao, Qiang||Huang, Tingwen||Zeng, Zhigang

publication date

• June 2019

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• Discrete-time Neural Network (nn)
• Inertial Nn
• STABILITY
• Synchronization
• Time Delay
• Timescale

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2018.2874982

start page

• 1854

end page

• 1866

volume

• 30

issue

• 6

URL

• http://dx.doi.org/10.1109/tnnls.2018.2874982