Exponential Stability of Complex-Valued Memristive Recurrent Neural Networks. Academic Article

abstract

• In this brief, we establish a novel complex-valued memristive recurrent neural network (CVMRNN) to study its stability. As a generalization of real-valued memristive neural networks, CVMRNN can be separated into real and imaginary parts. By means of M -matrix and Lyapunov function, the existence, uniqueness, and exponential stability of the equilibrium point for CVMRNNs are investigated, and sufficient conditions are presented. Finally, the effectiveness of obtained results is illustrated by two numerical examples.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Wang, H., Duan, S., Huang, T., Wang, L., & Li, C.

citation count

• 136

complete list of authors

• Wang, Huamin||Duan, Shukai||Huang, Tingwen||Wang, Lidan||Li, Chuandong

publication date

• March 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Complex-valued Memristive Neural Networks (cvmnns)
• Exponential Stability
• Lyapunov Function
• M-matrix

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2015.2513001

start page

• 766

end page

• 771

volume

• 28

issue

• 3

URL

• http://dx.doi.org/10.1109/tnnls.2015.2513001