Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters

2021 ◽  
Vol 11 (5/6) ◽  
pp. 527
Author(s):  
R. Krishnasamy ◽  
Raju K. George
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Asymptotic Stability ◽  
Mean Square ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Inertial Neural Networks

scholarly journals Finite-Time Stabilization for Stochastic Inertial Neural Networks with Time-Delay via Nonlinear Delay Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Deyi Li ◽  
Yuanyuan Wang ◽  
Guici Chen ◽  
Shasha Zhu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Design Method ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Nonlinear Feedback ◽  
Inertial Neural Networks ◽  
Finite Time Stabilization

This paper pays close attention to the problem of finite-time stabilization related to stochastic inertial neural networks with or without time-delay. By establishing proper Lyapunov-Krasovskii functional and making use of matrix inequalities, some sufficient conditions on finite-time stabilization are obtained and the stochastic settling-time function is also estimated. Furthermore, in order to achieve the finite-time stabilization, both delayed and nondelayed nonlinear feedback controllers are designed, respectively, in terms of solutions to a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate the correction of the theoretical results and the effectiveness of the proposed control design method.

scholarly journals OPTIMAL FILTERING IN DISCRETE-TIME SYSTEMS WITH TIME DELAYS AND MARKOVIAN JUMP PARAMETERS

2009 ◽  
Vol 51 (2) ◽  
pp. 218-233 ◽  
Author(s):  
CHUNYAN HAN ◽  
HUANSHUI ZHANG
Keyword(s):  
Discrete Time ◽  
Minimum Mean Square Error ◽  
Mean Square ◽  
Markovian Jump ◽  
Free System ◽  
Markovian Jump Parameters ◽  
Markovian Jump Linear Systems ◽  
Measurement Delay ◽  
Innovation Analysis ◽  
Delayed Measurements

AbstractThis paper investigates the linear minimum mean-square error estimation for discrete-time Markovian jump linear systems with delayed measurements. The key technique applied for treating the measurement delay is reorganization innovation analysis, by which the state estimation with delayed measurements is transformed into a standard linear mean-square filter of an associated delay-free system. The optimal filter is derived based on the innovation analysis method together with geometric arguments in an appropriate Hilbert space. The solution is given in terms of two Riccati difference equations. Finally, a simulation example is presented to illustrate the efficiency of the proposed method.

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