Exponential state estimation for reaction-diffusion inertial neural networks via incomplete measurement scheme

2021 ◽  
pp. 1-19
Author(s):  
Xuemei Wang ◽  
Xiaona Song ◽  
Jingtao Man ◽  
Nana Wu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Reaction Diffusion ◽  
Measurement Scheme ◽  
Incomplete Measurement ◽  
Inertial Neural Networks

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.

scholarly journals Dynamic behaviors for inertial neural networks with reaction-diffusion terms and distributed delays

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Famei Zheng
Keyword(s):  
Neural Networks ◽  
Equilibrium Point ◽  
Existence And Uniqueness ◽  
Global Exponential Stability ◽  
Degree Theory ◽  
Reaction Diffusion ◽  
Distributed Delays ◽  
Sufficient Condition ◽  
Topological Degree Theory ◽  
Inertial Neural Networks

AbstractA class of inertial neural networks (INNs) with reaction-diffusion terms and distributed delays is studied. The existence and uniqueness of the equilibrium point for the considered system is obtained by topological degree theory, and a sufficient condition is given to guarantee global exponential stability of the equilibrium point. Finally, an example is given to show the effectiveness of the results in this paper.

Memory-based State Estimation of T–S Fuzzy Markov Jump Delayed Neural Networks with Reaction–Diffusion Terms

2019 ◽  
Vol 50 (3) ◽  
pp. 2529-2546 ◽  
Author(s):  
Xiaona Song ◽  
Jingtao Man ◽  
Zhumu Fu ◽  
Mi Wang ◽  
Junwei Lu
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Reaction Diffusion ◽  
Markov Jump ◽  
Delayed Neural Networks
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