Finite-Time $$L_\infty $$ Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals

2019 ◽  
Vol 51 (2) ◽  
pp. 1379-1392 ◽  
Author(s):  
N. Gunasekaran ◽  
M. Syed Ali ◽  
S. Pavithra
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Sampled Data

scholarly journals Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Hongjie Li
Keyword(s):  
Neural Networks ◽  
Dynamic System ◽  
State Estimation ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Sampled Data ◽  
Information Communication ◽  
State Estimator ◽  
Event Triggered

The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme.

Exponential state estimation for delayed recurrent neural networks with sampled-data

2011 ◽  
Vol 69 (1-2) ◽  
pp. 555-564 ◽  
Author(s):  
Nan Li ◽  
Jiawen Hu ◽  
Jiming Hu ◽  
Lin Li
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Recurrent Neural Networks ◽  
Sampled Data

Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities

2021 ◽  
Vol 432 ◽  
pp. 240-249
Author(s):  
Yao Wang ◽  
Shengyuan Xu ◽  
Yongmin Li ◽  
Yuming Chu ◽  
Zhengqiang Zhang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Finite Time ◽  
Markovian Jump ◽  
Markovian Jump Neural Networks

scholarly journals Extended dissipativity and non-fragile synchronization for Recurrent neural networks with multiple time-varying delays via sampled-data control

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
R. Anbuvithya ◽  
S. Dheepika Sri ◽  
R. Vadivel ◽  
Nallappan Gunasekaran ◽  
P. Hammachukiattikul
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Multiple Time ◽  
Time Varying ◽  
Sampled Data ◽  
Data Control ◽  
Sampled Data Control

Finite-time synchronization control of a class of memristor-based recurrent neural networks

2015 ◽  
Vol 63 ◽  
pp. 133-140 ◽  
Author(s):  
Minghui Jiang ◽  
Shuangtao Wang ◽  
Jun Mei ◽  
Yanjun Shen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Synchronization Control

$$H_{\infty }$$ H ∞ State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval

2018 ◽  
Vol 50 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Liang Shen ◽  
Hao Shen ◽  
Mingming Gao ◽  
Yajuan Liu ◽  
Xia Huang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Fading Channels ◽  
Finite Time ◽  
Time Interval ◽  
Finite Time Interval
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