IEEE transaction on neural networks and learning systems, 2017-03, Vol.28 (3), p.766-771
2017
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Exponential Stability of Complex-Valued Memristive Recurrent Neural Networks
- Ist Teil von
- IEEE transaction on neural networks and learning systems, 2017-03, Vol.28 (3), p.766-771
- Ort / Verlag
- United States: IEEE
- Erscheinungsjahr
- 2017
- Quelle
- IEEE Xplore
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2162-237XeISSN: 2162-2388DOI: 10.1109/TNNLS.2015.2513001Titel-ID: cdi_ieee_primary_7373657
- Format
- –
- Schlagworte
- <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">M -matrix, 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