Delay-dependent and delay-independent passivity of a class of recurrent neural networks with impulse and multi-proportional delays

2018 ◽  
Vol 308 ◽  
pp. 235-244 ◽  
Author(s):  
Liqun Zhou
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Proportional Delays ◽  
Delay Dependent

scholarly journals Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ding ◽  
Hong-Bing Zeng ◽  
Wei Wang ◽  
Fei Yu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Improved Stability ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability ◽  
Varying Delay

This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.

Improved delay-dependent stability criteria for recurrent neural networks with time-varying delays

2014 ◽  
Vol 129 ◽  
pp. 401-408 ◽  
Author(s):  
Xiangbing Zhou ◽  
Junkang Tian ◽  
Hongjiang Ma ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Delay Dependent ◽  
Delay Dependent Stability
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