Novel global robust exponential stability criterion for uncertain inertial-type BAM neural networks with discrete and distributed time-varying delays via Lagrange sense

2018 ◽  
Vol 355 (11) ◽  
pp. 4727-4754 ◽  
Author(s):  
C. Maharajan ◽  
R. Raja ◽  
Jinde Cao ◽  
G. Rajchakit
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Stability Criterion ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Robust Exponential Stability ◽  
Global Robust Exponential Stability

Global Robust Exponential Stability of Discrete-Time BAM Neural Networks with Time Varying Delays

2012 ◽  
Vol 546-547 ◽  
pp. 772-777 ◽  
Author(s):  
Rui Zhang ◽  
Jian Liu ◽  
Ying Zhang ◽  
Chang Tao Wang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Robust Exponential Stability ◽  
Discrete Lyapunov Functional ◽  
Inequality Techniques ◽  
Global Robust Exponential Stability

In this paper, the global robust exponential stability is discussed for discrete-time bidirectional associative memory (BAM) neural networks with time varying delays. By the linear matrix inequality (LMI) technique and discrete Lyapunov functional combined with inequality techniques, a new global exponential stability criterion of the equilibrium point is obtained for this system. The proposed result is less restrictive, and easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation example is used to demonstrate the effectiveness of our result.

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