Global Exponential Stability of Memristive Neural Networks With Mixed Time-Varying Delays. Academic Article

abstract

• This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Sheng, Y., Huang, T., Zeng, Z., & Miao, X.

citation count

• 12

complete list of authors

• Sheng, Yin||Huang, Tingwen||Zeng, Zhigang||Miao, Xiangshui

publication date

• August 2021

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• Biological Neural Networks
• CONTROL THEORY
• Circuit Stability
• Delay Effects
• Delays
• Discrete-time Delay
• Distributed Time Delay
• Lagrange Exponential Stability
• Lyapunov Exponential Stability
• Memristive Neural Networks (mnns)
• Stability Analysis

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2020.3015944

start page

• 3690

end page

• 3699

volume

• 32

issue

• 8

URL

• http://dx.doi.org/10.1109/tnnls.2020.3015944