Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 28, Issue 3, Pages 766-771Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2513001
Keywords
Complex-valued memristive neural networks (CVMNNs); exponential stability; Lyapunov function; M-matrix
Categories
Funding
- Fundamental Research Funds for the Central Universities [XDJK2016A001, XDJK2014A009]
- Program for New Century Excellent Talents in University [[2013]47]
- Qatar National Research Fund through the National Priorities Research Program (a member of Qatar Foundation) [NPRP 4-1162-1-181]
- Excellent Talents Program in Scientific and Technological Activities for Overseas Scholars within the Ministry of Personnel, China [2012-186]
- National Natural Science Foundation of China [61372139, 61503175, 61571372, 61374078, 61101233, 60972155]
- High School Key Scientific Research Project of Henan Province [15A120013]
- Spring Sunshine Plan Research Project within the Ministry of Education of China [z2011148]
- University Excellent Talents Supporting Foundations of Chongqing [2011-65]
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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.
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