Finite-Time Boundedness Analysis of Uncertain Neural Networks with Time Delay: An LMI Approach

2007 ◽  
pp. 904-909 ◽  
Author(s):  
Yanjun Shen ◽  
Lin Zhu ◽  
Qi Guo
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Lmi Approach ◽  
Uncertain Neural Networks ◽  
Finite Time Boundedness

Finite-time boundedness for time-delay neural networks with external disturbances via weight learning approaches

2019 ◽  
Vol 33 (28) ◽  
pp. 1950343 ◽  
Author(s):  
Zhilian Yan ◽  
Youmei Zhou ◽  
Xia Huang ◽  
Jianping Zhou
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite Time ◽  
Weight Matrix ◽  
External Disturbances ◽  
Connection Weight ◽  
Learning Rules ◽  
Finite Time Boundedness ◽  
Weight Learning ◽  
Matrix Case

This paper addresses the issue of finite-time boundedness for time-delay neural networks with external disturbances via weight learning. With the help of a group of inequalities and combining with the Lyapunov theory, weight learning rules are devised to ensure the neural networks to be finite-time bounded for the fixed connection weight matrix case and the fixed delayed connection weight matrix case, respectively. Sufficient conditions on the existence of the desired learning rules are presented in the form of linear matrix inequalities, which are easily verified by MATLAB software. It is shown that the proposed learning rules also guarantee the finite-time stability of the time-delay neural networks. Finally, a numerical example is employed to show the applicability of the devised weight learning rules.

scholarly journals Finite-Time Boundedness for a Class of Delayed Markovian Jumping Neural Networks with Partly Unknown Transition Probabilities

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Li Liang
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Transition Probabilities ◽  
The State ◽  
Time Interval ◽  
Finite Time Interval ◽  
Markovian Jumping ◽  
Stochastic Boundedness ◽  
Partly Unknown Transition Probabilities ◽  
Finite Time Boundedness

This paper is concerned with the problem of finite-time boundedness for a class of delayed Markovian jumping neural networks with partly unknown transition probabilities. By introducing the appropriate stochastic Lyapunov-Krasovskii functional and the concept of stochastically finite-time stochastic boundedness for Markovian jumping neural networks, a new method is proposed to guarantee that the state trajectory remains in a bounded region of the state space over a prespecified finite-time interval. Finally, numerical examples are given to illustrate the effectiveness and reduced conservativeness of the proposed results.

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.

Finite-time passivity of multiple weighted coupled uncertain neural networks with directed and undirected topologies

2019 ◽  
Vol 367 ◽  
pp. 217-225 ◽  
Author(s):  
Xiao-Xiao Zhang ◽  
Jin-Liang Wang ◽  
Yu Zhang ◽  
Shao-Qing Fan
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Uncertain Neural Networks

scholarly journals New Results on Passivity Analysis for Uncertain Neural Networks with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xueying Shao ◽  
Qing Lu ◽  
Hamid Reza Karimi ◽  
Jin Zhu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Linear Matrix ◽  
Delta Operator ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Uncertain Neural Networks ◽  
The Stability ◽  
Delta Operator System ◽  
Varying Delay

The paper investigates the stability and passivity analysis problems for a class of uncertain neural networks with time-delay via delta operator approach. Both the parameter uncertainty and the generalized activation functions are considered in this paper. By constructing an appropriate Lyapunov-Krasovskii functional, some new stability and passivity conditions are obtained in terms of linear matrix inequalities (LMIs). The main characteristic of this paper is to obtain novel stability and passivity analysis criteria for uncertain neural networks with time-delay in the delta operator system framework. A numerical example is presented to demonstrate the effectiveness of the proposed results.

Observer-based H∞ finite-time control for switched linear systems with interval time-delay

2018 ◽  
Vol 41 (5) ◽  
pp. 1348-1360 ◽  
Author(s):  
Gökhan Göksu ◽  
Ulviye Başer
Keyword(s):  
Time Delay ◽  
Finite Time ◽  
Dwell Time ◽  
Sufficient Conditions ◽  
Average Dwell Time ◽  
Linearization Method ◽  
Interval Time ◽  
Time Control ◽  
Finite Time Boundedness ◽  
Time Bounds

In this work, interval time-delay switched systems having completely unstable and mixed stable matrices of the state vector are considered. An observer-based controller is designed for finite-time boundedness and H∞-control of these systems. New sufficient conditions on the existence of a desired observer are developed and new average dwell-time bounds are introduced separately in case of unstable and mixed stable subsystems. An algorithm is presented for the calculation of unknown constants in the average dwell-time bounds which depend on nonlinear matrices in terms of the cone complementarity linearization method. Finally, numerical examples are given for the effectiveness and validity of the proposed solutions.

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