Exponential State Estimation for Stochastically Disturbed Discrete-Time Memristive Neural Networks: Multiobjective Approach
| Autor: | Jinde Cao, Xing-Bao Gao, Ruoxia Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer Networks and Communications Stochastic process Linear matrix inequality 02 engineering and technology Function (mathematics) Stability (probability) Computer Science Applications Exponential function Matrix (mathematics) Discrete time and continuous time Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Applied mathematics 020201 artificial intelligence & image processing Software Mathematics |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 31:3168-3177 |
| ISSN: | 2162-2388 2162-237X |
| DOI: | 10.1109/tnnls.2019.2938774 |
| Popis: | The state estimation of the discrete-time memristive model is studied in this article. By applying the stochastic analysis technique, sufficient formulas are established to ensure the exponentially mean-square stability of the error model. Moreover, the derived control gain matrix can be calculated via the linear matrix inequality (LMI). It should be mentioned that, by extending the derived conclusion to a multiobjective optimization problem, the maximum bound of the active function and the minimum bound of the disturbance attenuation are derived. The corresponding simulation figures are provided in the end. |
| Databáze: | OpenAIRE |
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