Name
Title | ||
|---|---|---|
Time-varying delay-dependent finite-time boundedness with H∞ performance for Markovian jump neural networks with state and input constraints |
Abstract | ||
|---|---|---|
This work focuses on time-varying delay-dependent finite-time boundedness with H∞ performance for Markovian jump neural networks with state and input constraints. This is the first try to study finite-time boundedness with H∞ performance for Markovian jump neural networks with state and input constraints. There are disturbance input and system state with bounded peaks in the nonlinear system. In contrast to the existing study results, the studied system can be applied to a wider range. The purpose is to obtain less conservative conditions on finite-time H∞ performance for Markovian jump neural networks with time-varying delay. Sufficient conditions of finite-time boundedness with H∞ performance are collected by stochastic Lyapunov function. Linear matrix inequalities are utilized to obtain analysis results. Eventually, an example is provided to illustrate the validity of the addressed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2020.10.088 | Neurocomputing |
Keywords | DocType | Volume |
Markovian jump neural networks,Time-varying delay,Finite-time boundedness (FTB),H∞ performance,Linear matrix inequalities (LMIs) | Journal | 423 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaoxin Sun | 1 | 56 | 11.79 |
| huaguang zhang | 2 | 505 | 39.89 |
| Li Weihua | 3 | 32 | 20.76 |
| Yingchun Wang | 4 | 529 | 32.27 |