Extended dissipativity analysis for discrete-time delayed neural networks based on an extended reciprocally convex matrix inequality

2018 ◽  
Vol 462 ◽  
pp. 357-366 ◽  
Author(s):  
Li Jin ◽  
Yong He ◽  
Lin Jiang ◽  
Min Wu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Matrix Inequality ◽  
Delayed Neural Networks ◽  
Dissipativity Analysis

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.

Novel results on dissipativity analysis for generalized delayed neural networks

2019 ◽  
Vol 332 ◽  
pp. 328-338 ◽  
Author(s):  
Can Zhao ◽  
Shouming Zhong ◽  
Xiaojun Zhang ◽  
Kaibo Shi
Keyword(s):  
Neural Networks ◽  
Delayed Neural Networks ◽  
Dissipativity Analysis

scholarly journals Dynamical Analysis of DTNN with Impulsive Effect

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Chao Chen ◽  
Zhenkun Huang ◽  
Honghua Bin ◽  
Xiaohui Liu
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Dynamical Analysis ◽  
Impulsive Effect ◽  
Decomposition Approach ◽  
Delayed Neural Networks ◽  
Delay Difference ◽  
New Criteria

We present dynamical analysis of discrete-time delayed neural networks with impulsive effect. Under impulsive effect, we derive some new criteria for the invariance and attractivity of discrete-time neural networks by using decomposition approach and delay difference inequalities. Our results improve or extend the existing ones.

scholarly journals Synchronization of Chaotic Delayed Neural Networks via Impulsive Control

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Fang ◽  
Kang Yan ◽  
Kelin Li
Keyword(s):  
Neural Networks ◽  
Impulsive Control ◽  
Sufficient Conditions ◽  
Stability Theorem ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Lyapunov Stability Theorem ◽  
Delayed Neural Networks ◽  
Impulsive Synchronization ◽  
Synchronization Problem

This paper is concerned with the impulsive synchronization problem of chaotic delayed neural networks. By employing Lyapunov stability theorem, impulsive control theory and linear matrix inequality (LMI) technique, several new sufficient conditions ensuring the asymptotically synchronization for coupled chaotic delayed neural networks are derived. Based on these new sufficient conditions, an impulsive controller is designed. Moreover, the stable impulsive interval of synchronized neural networks is objectively estimated by combining the MATLAB LMI toolbox and one of the two given equations. Two examples with numerical simulations are given to illustrate the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document