Flip Bifurcation of a Class of Discrete-time Neural Networks

2009 ◽  
Author(s):  
Rong Yu ◽  
Xiaoliang Zhou
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Flip Bifurcation

Discrete-time super-twisting controller using neural networks

2021 ◽  
Author(s):  
M. Hernandez–Gonzalez ◽  
E.A. Hernandez–Vargas
Keyword(s):  
Neural Networks ◽  
Discrete Time

scholarly journals Global Dissipativity on Uncertain Discrete-Time Neural Networks with Time-Varying Delays

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Qiankun Song ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Computationally Efficient ◽  
Inequality Technique ◽  
General Activation ◽  
Delay Dependent

The problems on global dissipativity and global exponential dissipativity are investigated for uncertain discrete-time neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Illustrated examples are given to show the effectiveness of the proposed criteria. It is noteworthy that because neither model transformation nor free-weighting matrices are employed to deal with cross terms in the derivation of the dissipativity criteria, the obtained results are less conservative and more computationally efficient.

Sign in / Sign up

Export Citation Format

Share Document