scholarly journals Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

2022 ◽  
Vol 412 ◽  
pp. 126583
Author(s):  
Qiang Li ◽  
Jinling Liang
Keyword(s):  
State Estimation ◽  
Discrete Time ◽  
Markovian Switching ◽  
Mode Detection ◽  
Asynchronous State ◽  
Discrete Time Case

Finite-time H∞ asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements

2019 ◽  
Vol 356 ◽  
pp. 113-128 ◽  
Author(s):  
Hao Shen ◽  
Mengping Xing ◽  
Shicheng Huo ◽  
Zheng-Guang Wu ◽  
Ju H. Park
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Discrete Time ◽  
Finite Time ◽  
Markov Jump ◽  
Asynchronous State

scholarly journals $H_\infty$ State Estimation of Delayed Recurrent Memristive Neural Networks: continuous-time case and discrete-time case

2021 ◽  
Author(s):  
fangyuan Ma ◽  
Xingbao Gao
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Discrete Time ◽  
Continuous Time ◽  
Estimation Error ◽  
Linear Matrix ◽  
Memristive Neural Networks ◽  
Connection Weight ◽  
Discrete Time Case ◽  
The Given

This paper investigates the problem of $H_\infty$ state estimation of delayed recurrent memristive neural networks (DRMNNs) with both continuous-time and discrete-time cases. By utilizing Lyapunov-Krasovskii functional (LKF) and linear matrix inequalities (LMIs), two criterions are provided to guarantee the asymptotically stable of the estimation error systems with a $H_\infty$ performance. The connection weight parameters of DRMNNs are dealed with logical switching signals, which greatly reduces the computational complexity. The given conditions can be easily checked by solving LMIs, the obtained theoretical results are supported demonstrated by two numerical examples.

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