New results on robust finite-time boundedness of uncertain switched neural networks with time-varying delays

2015 ◽  
Vol 151 ◽  
pp. 522-530 ◽  
Author(s):  
Shun Wang ◽  
Tiange Shi ◽  
Ming Zeng ◽  
Lixian Zhang ◽  
Fuad E. Alsaadi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Varying ◽  
Switched Neural Networks ◽  
Finite Time Boundedness

Finite-time boundedness and stabilization of uncertain switched neural networks with time-varying delay

2015 ◽  
Vol 69 ◽  
pp. 135-143 ◽  
Author(s):  
Yuanyuan Wu ◽  
Jinde Cao ◽  
Abdulaziz Alofi ◽  
Abdullah AL-Mazrooei ◽  
Ahmed Elaiw
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Varying ◽  
Time Varying Delay ◽  
Switched Neural Networks ◽  
Finite Time Boundedness ◽  
Varying Delay

scholarly journals H_∞ Finite-time Boundedness for Discrete-time Delay Neural Networks via Reciprocally Convex Approach

2020 ◽  
Vol 36 (3) ◽  
Author(s):  
Le Anh Tuan
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Finite Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability ◽  
Discrete Time Delay ◽  
Finite Time Boundedness ◽  
Reciprocally Convex Approach

This paper addresses the problem of finite-time boundedness for discrete-time neural networks with interval-like time-varying delays. First, a delay-dependent finite-time boundedness criterion under the finite-time  performance index for the system is given based on constructing a set of adjusted Lyapunov–Krasovskii functionals and using reciprocally convex approach. Next, a sufficient condition is drawn directly which ensures the finite-time stability of the corresponding nominal system. Finally, numerical examples are provided to illustrate the validity and applicability of the presented conditions. Keywords: Discrete-time neural networks,  performance, finite-time stability, time-varying delay, linear matrix inequality.  

Finite-time boundedness of state estimation for neural networks with time-varying delays

2014 ◽  
Vol 129 ◽  
pp. 257-264 ◽  
Author(s):  
Jun Cheng ◽  
Shouming Zhong ◽  
Qishui Zhong ◽  
Hong Zhu ◽  
Yuanhua Du
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Finite Time ◽  
Time Varying ◽  
Finite Time Boundedness

Stochastic finite-time boundedness for Markovian jumping neural networks with time-varying delays

2014 ◽  
Vol 242 ◽  
pp. 281-295 ◽  
Author(s):  
Jun Cheng ◽  
Hong Zhu ◽  
Yucai Ding ◽  
Shouming Zhong ◽  
Qishui Zhong
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Time Varying ◽  
Markovian Jumping ◽  
Finite Time Boundedness

scholarly journals Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Shanmugam Saravanan ◽  
M. Syed Ali ◽  
Ahmed Alsaedi ◽  
Bashir Ahmad
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Auxiliary Function ◽  
Integral Inequalities ◽  
Time Varying ◽  
Numerical Examples ◽  
Finite Time Boundedness

In this paper, we investigated the problem of the finite-time boundedness and finitetime passivity for neural networks with time-varying delays. A triple, quadrable and five integral terms with the delay information are introduced in the new Lyapunov–Krasovskii functional (LKF). Based on the auxiliary integral inequality, Writinger integral inequality and Jensen’s inequality, several sufficient conditions are derived. Finally, numerical examples are provided to verify the effectiveness of the proposed criterion. There results are compared with the existing results. 

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