Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays

2017 ◽  
Vol 311 ◽  
pp. 100-117 ◽  
Author(s):  
Runan Guo ◽  
Ziye Zhang ◽  
Xiaoping Liu ◽  
Chong Lin
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Delays ◽  
Bam Neural Networks ◽  
Complex Valued

Impulsive Cohen–Grossberg BAM neural networks with mixed time-delays: An exponential stability analysis issue

2018 ◽  
Vol 275 ◽  
pp. 2588-2602 ◽  
Author(s):  
C. Maharajan ◽  
R. Raja ◽  
Jinde Cao ◽  
G. Rajchakit ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Delays ◽  
Bam Neural Networks ◽  
Mixed Time Delays

scholarly journals Stability Analysis for Memristor-Based Complex-Valued Neural Networks with Time Delays

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 120
Author(s):  
Ping Hou ◽  
Jun Hu ◽  
Jie Gao ◽  
Peican Zhu
Keyword(s):  
Numerical Simulation ◽  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Activation Functions ◽  
Complex Valued ◽  
Theoretical Results

In this paper, the problem of stability analysis for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is investigated extensively. This paper focuses on the exponential stability of the MCVNNs with time-varying delays. By means of the Brouwer’s fixed-point theorem and M-matrix, the existence, uniqueness, and exponential stability of the equilibrium point for MCVNNs are studied, and several sufficient conditions are obtained. In particular, these results can be applied to general MCVNNs whether the activation functions could be explicitly described by dividing into two parts of the real parts and imaginary parts or not. Two numerical simulation examples are provided to illustrate the effectiveness of the theoretical results.

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