Generalised state estimation of Markov jump neural networks based on the Bessel–Legendre inequality

2019 ◽  
Vol 13 (9) ◽  
pp. 1284-1290 ◽  
Author(s):  
Hao Shen ◽  
Shiyu Jiao ◽  
Jianwei Xia ◽  
Ju H. Park ◽  
Xia Huang
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump

Event-triggered Non-fragile State Estimation for Discrete Nonlinear Markov Jump Neural Networks with Sensor Failures

2019 ◽  
Vol 17 (5) ◽  
pp. 1131-1140 ◽  
Author(s):  
Jianning Li ◽  
Zhujian Li ◽  
Yufei Xu ◽  
Kaiyang Gu ◽  
Wendong Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump ◽  
Fragile State ◽  
Sensor Failures ◽  
Event Triggered

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

Extended dissipative-based state estimation for Markov jump coupled neural networks with reaction-diffusion terms

2021 ◽  
pp. 014233122110282
Author(s):  
Dongxiao Hu ◽  
Xiaona Song ◽  
Xingru Li
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Dynamic Error ◽  
Signal Transmission ◽  
Reaction Diffusion ◽  
Linear Matrix ◽  
Markov Jump ◽  
Memory Controller ◽  
Coupled Neural Networks ◽  
Error System

This work mainly concentrates on the state estimation for Markov jump coupled neural networks (MJCNNs) with reaction-diffusion terms, in which the memory controller is employed. First, the considered MJCNNs model is introduced, and the dynamic error system can be obtained based on the proposed state estimator. Then, a memory controller that involves constant signal transmission delay is designed. Moreover, by Lyapunov functional method, inequality technique and Kronecker product law, a novel stable and extended dissipative analysis criteria can be established to ensure that the stability of the error system the error system. Meanwhile, the controller gains can be obtained by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the developed method.

Event-triggered dissipative state estimation for Markov jump neural networks with random uncertainties

2019 ◽  
Vol 356 (17) ◽  
pp. 10155-10178 ◽  
Author(s):  
Jing Wang ◽  
Mengping Xing ◽  
Yonghui Sun ◽  
Jianzhen Li ◽  
Junwei Lu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Markov Jump ◽  
Random Uncertainties ◽  
Event Triggered ◽  
Dissipative State
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