Synchronization stability of memristor-based complex-valued neural networks with time delays
| Autor: | Dan Liu, Song Zhu, Er Ye |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Time delays Time Factors Artificial neural network Property (programming) Computer science Cognitive Neuroscience Stability (learning theory) 02 engineering and technology Memristor law.invention Nonlinear system 020901 industrial engineering & automation Artificial Intelligence Control theory law Synchronization (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Neural Networks Computer Algorithms |
| Zdroj: | Neural Networks. 96:115-127 |
| ISSN: | 0893-6080 |
| Popis: | This paper focuses on the dynamical property of a class of memristor-based complex-valued neural networks (MCVNNs) with time delays. By constructing the appropriate Lyapunov functional and utilizing the inequality technique, sufficient conditions are proposed to guarantee exponential synchronization of the coupled systems based on drive-response concept. The proposed results are very easy to verify, and they also extend some previous related works on memristor-based real-valued neural networks. Meanwhile, the obtained sufficient conditions of this paper may be conducive to qualitative analysis of some complex-valued nonlinear delayed systems. A numerical example is given to demonstrate the effectiveness of our theoretical results. |
| Databáze: | OpenAIRE |
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