Stability Analysis for a Class of Impulsive Bidirectional Associative Memory (BAM) Neural Networks with Distributed Delays and Leakage Time-Varying Delays

2018 ◽  
Vol 50 (1) ◽  
pp. 851-885 ◽  
Author(s):  
Chaouki Aouiti ◽  
El Abed Assali
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Associative Memory ◽  
Distributed Delays ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory

scholarly journals Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wei Feng ◽  
Simon X. Yang ◽  
Haixia Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Associative Memory ◽  
Robust Stability ◽  
Neutral Type ◽  
Time Varying ◽  
Bidirectional Associative Memory ◽  
Numerical Examples ◽  
Network Parameters ◽  
Robust Stability Analysis

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Global exponential stability analysis of anti-periodic solutions of discontinuous bidirectional associative memory (BAM) neural networks with time-varying delays

2020 ◽  
Vol 21 (7-8) ◽  
pp. 807-820
Author(s):  
Xiangying Fu ◽  
Fanchao Kong
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Periodic Solutions ◽  
Associative Memory ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Globally Exponential Stability ◽  
Discontinuous Activations ◽  
Neural Network System

AbstractThis paper is concerned with a class of bidirectional associative memory (BAM) neural networks with discontinuous activations and time-varying delays. Under the basic framework of differential inclusions theory, the existence result of solutions in sense of Filippov solution is firstly established by using the fundamental solution matrix of coefficients and inequality analysis technique. Also, the boundness of the solutions can be estimated. Secondly, based on the non-smooth Lyapunov-like approach and by construsting suitable Lyapunov–Krasovskii functionals, some new sufficient criteria are given to ascertain the globally exponential stability of the anti-periodic solutions for the proposed neural network system. Furthermore, we have collated our effort with some previous existing ones in the literatures and showed that it can take more advantages. Finally, two examples with numerical simulations are exploited to illustrate the correctness.

scholarly journals Stability analysis of almost periodic solutions for discontinuous bidirectional associative memory (BAM) neural networks with discrete and distributed delays

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Weijun Xie ◽  
Fanchao Kong ◽  
Hongjun Qiu ◽  
Xiangying Fu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Periodic Solutions ◽  
Associative Memory ◽  
Stability Result ◽  
Distributed Delays ◽  
Bam Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Bidirectional Associative Memory

AbstractThis paper aims to discuss a class of discontinuous bidirectional associative memory (BAM) neural networks with discrete and distributed delays. By using the set-valued map, differential inclusions theory and fundamental solution matrix, the existence of almost-periodic solutions for the addressed neural network model is firstly discussed under some new conditions. Subsequently, based on the non-smooth analysis theory with Lyapunov-like strategy, the global exponential stability result of the almost-periodic solution for the proposed neural network system is also established without using any additional conditions. The results achieved in the paper extend some previous works on BAM neural networks to the discontinuous case and it is worth mentioning that it is the first time to investigate the almost-periodic dynamic behavior for the BAM neural networks like the form in this paper. Finally, in order to demonstrate the effectiveness of the theoretical schemes, simulation results of two topical numerical examples are delineated.

scholarly journals Stability Analysis of Fractional-Order Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22
Author(s):  
Zhanying Yang ◽  
Jie Zhang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Fractional Order ◽  
Associative Memory ◽  
Analytical Techniques ◽  
Mean Value Theorem ◽  
Sufficient Condition ◽  
Time Varying ◽  
Bidirectional Associative Memory ◽  
Practical Applications

This paper studies the stability analysis of fractional-order bidirectional associative memory neural networks with mixed time-varying delays. The orders of these systems lie in the interval 1,2. Firstly, a sufficient condition is derived to ensure the finite-time stability of systems by resorting to some analytical techniques and some elementary inequalities. Next, a sufficient condition is obtained to guarantee the global asymptotic stability of systems based on the Laplace transform, the mean value theorem, the generalized Gronwall inequality, and some properties of Mittag–Leffler functions. In particular, these obtained conditions are expressed as some algebraic inequalities which can be easily calculated in practical applications. Finally, some numerical examples are given to verify the feasibility and effectiveness of the obtained main results.

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