Exponential Stability of Complex-Valued Memristive Recurrent Neural Networks

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, uni...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2017-03, Vol.28 (3), p.766-771
Main Authors: Wang, Huamin, Duan, Shukai, Huang, Tingwen, Wang, Lidan, Li, Chuandong
Format: Article
Language:English
Subjects:
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Biological neural networks
Complex-valued memristive neural networks (CVMNNs)
Control theory
Economic models
exponential stability
Learning systems
Liapunov functions
Lyapunov function
Memristors
Neural networks
Recurrent neural networks
Stability
Stability analysis
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Summary: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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2015.2513001