Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays
| Autor: | Ramalingam Sriraman, Yang Cao, Rajendran Samidurai |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Numerical Analysis
Class (set theory) General Computer Science Artificial neural network Computer science Applied Mathematics Probabilistic logic Stability (learning theory) Complex valued 010103 numerical & computational mathematics 02 engineering and technology Linear matrix 01 natural sciences Theoretical Computer Science Exponential stability Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing 0101 mathematics MATLAB computer computer.programming_language |
| Zdroj: | Mathematics and Computers in Simulation. 171:103-118 |
| ISSN: | 0378-4754 |
| DOI: | 10.1016/j.matcom.2019.04.001 |
| Popis: | This paper studies the global asymptotic stability problem for a class of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays as well as stochastic disturbances. Based on the Lyapunov–Krasovskii functional (LKF) method and mathematical analytic techniques, delay-dependent stability criteria are derived by separating complex-valued neural networks (CVNNs) into real and imaginary parts. Furthermore, the obtained sufficient conditions are presented in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two simulation examples are provided to show the effectiveness and advantages of the proposed results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect