The optimal state estimation for competitive neural network with time-varying delay using Local Search Algorithm

2020 ◽  
Vol 540 ◽  
pp. 123102
Author(s):  
Zhicheng Shi ◽  
Yongqing Yang ◽  
Qi Chang ◽  
Xianyun Xu
Keyword(s):  
Neural Network ◽  
Local Search ◽  
State Estimation ◽  
Search Algorithm ◽  
Time Varying ◽  
Local Search Algorithm ◽  
Competitive Neural Network ◽  
Time Varying Delay ◽  
Optimal State Estimation ◽  
Varying Delay

scholarly journals State Estimation for a Class of Distributed Parameter Systems with Time-Varying Delay over Mobile Sensor–Actuator Networks with Missing Measurements

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 661
Author(s):  
Huansen Fu ◽  
Baotong Cui ◽  
Bo Zhuang ◽  
Jianzhong Zhang
Keyword(s):  
State Estimation ◽  
Distributed Parameter Systems ◽  
Operator Semigroup ◽  
Distributed Parameter ◽  
Time Varying ◽  
Mobile Sensor ◽  
Missing Measurements ◽  
Time Varying Delay ◽  
Sensor Actuator ◽  
Varying Delay

This work proposes a state estimation strategy over mobile sensor–actuator networks with missing measurements for a class of distributed parameter systems (DPSs) with time-varying delay. Initially, taking advantage of the abstract development equation theory and operator semigroup method, this kind of delayed DPSs described by partial differential equations (PDEs) is derived for evolution equations. Subsequently, the distributed state estimators including consistency component and gain component are designed; the purpose is to estimate the original state distribution of the delayed DPSs with missing measurements. Then, a delay-dependent guidance approach is presented in the form of mobile control forces by constructing an appropriate Lyapunov function candidate. Furthermore, by applying Lyapunov stability theorem, operator semigroup theory, and a stochastic analysis approach, the estimation error systems have been proved asymptotically stable in the mean square sense, which indicates the estimators can approximate the original system states effectively when this kind of DPS has time-delay and the mobile sensors occur missing measurements. Finally, the correctness of control strategy is illustrated by numerical simulation results.

scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

An LPV based robust peak-to-peak state estimation for genetic regulatory networks with time varying delay

2015 ◽  
Vol 160 ◽  
pp. 261-273 ◽  
Author(s):  
Mohammad Mohammadian ◽  
Hamid Reza Momeni ◽  
Hazhar Sufi Karimi ◽  
Iman Shafikhani ◽  
Mahdieh Tahmasebi
Keyword(s):  
State Estimation ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Time Varying ◽  
Time Varying Delay ◽  
Varying Delay
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