Robust stability and $$H_{\infty}$$ H ∞ filter design for neutral stochastic neural networks with parameter uncertainties and time-varying delay

2015 ◽  
Vol 8 (2) ◽  
pp. 511-524 ◽  
Author(s):  
Mingang Hua ◽  
Huasheng Tan ◽  
Juntao Fei ◽  
Jianjun Ni
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Filter Design ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Time Varying Delay ◽  
Neutral Stochastic Neural Networks ◽  
Varying Delay

Delay-dependent robust stability criteria for stochastic neural networks of neutral-type with interval time-varying delay and linear fractional uncertainties

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
GUOQUAN LIU ◽  
SIMON X. YANG ◽  
YI CHAI ◽  
WEI FU
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Neutral Type ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Interval Time ◽  
Time Varying Delay ◽  
Delay Dependent ◽  
Varying Delay

In this paper, we investigate the problem of robust stability for a class of delayed neural networks of neutral-type with linear fractional uncertainties. The activation functions are assumed to be unbounded, non-monotonic and non-differentiable, and the delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of the interval time-varying delay are available. By constructing a general form of the Lyapunov–Krasovskii functional, and using the linear matrix inequality (LMI) approach, we derive several delay-dependent stability criteria in terms of LMI. Finally, we give a number of examples to illustrate the effectiveness of the proposed method.

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