New Algebraic Criteria for Global Exponential Periodicity and Stability of Memristive Neural Networks with Variable Delays
| Autor: | Dan Liu, Er Ye, Song Zhu, Shengwu Zhou |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer Networks and Communications Computer science General Neuroscience Computational intelligence 02 engineering and technology Stability (probability) Exponential function 020901 industrial engineering & automation Exponential stability Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing Algebraic number Software Variable (mathematics) Complement (set theory) |
| Zdroj: | Neural Processing Letters. 48:1749-1766 |
| ISSN: | 1573-773X 1370-4621 |
| Popis: | This paper concentrates on the problem of global exponential periodicity and stability of memristive neural networks with variable delays. By constructing the appropriate Lyapunov functionals and utilizing some inequality techniques, new algebraic criteria are proposed to guarantee the existence and global exponential stability of periodic solution of the considered system. In addition, the proposed theoretical results not only expand and complement the earlier publications, but also are easy to be checked with the parameters of system itself. A numerical example is given to demonstrate the effectiveness of our results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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