scholarly journals 2N almost periodic attractors for Cohen–Grossberg-type BAM neural networks with variable coefficients and distributed delays

2011 ◽  
Vol 373 (1) ◽  
pp. 322-342 ◽  
Author(s):  
Wei Ding ◽  
Linghai Wang
Keyword(s):  
Neural Networks ◽  
Variable Coefficients ◽  
Distributed Delays ◽  
Bam Neural Networks ◽  
Almost Periodic ◽  
Periodic Attractors

scholarly journals Global Stability of Almost Periodic Solution of a Class of Neutral-Type BAM Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Tetie Pan ◽  
Bao Shi ◽  
Jian Yuan
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Variable Coefficients ◽  
Almost Periodic Solution ◽  
Bam Neural Networks ◽  
Almost Periodic ◽  
Globally Exponential Stability ◽  
First Time

A class of BAM neural networks with variable coefficients and neutral delays are investigated. By employing fixed-point theorem, the exponential dichotomy, and differential inequality techniques, we obtain some sufficient conditions to insure the existence and globally exponential stability of almost periodic solution. This is the first time to investigate the almost periodic solution of the BAM neutral neural network and the results of this paper are new, and they extend previously known results.

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.

PERIODIC SOLUTIONS FOR A CLASS OF FUZZY BAM NEURAL NETWORKS WITH DISTRIBUTED DELAYS AND VARIABLE COEFFICIENTS

2010 ◽  
Vol 20 (05) ◽  
pp. 1551-1565 ◽  
Author(s):  
YONGKUN LI ◽  
LI YANG ◽  
WANQIN WU
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Periodic Solutions ◽  
Differential Inequality ◽  
Global Exponential Stability ◽  
Sufficient Conditions ◽  
Variable Coefficients ◽  
Distributed Delays ◽  
Bam Neural Networks ◽  
Fuzzy Bam Neural Networks

In this paper, we consider a class of fuzzy BAM neural networks with distributed delays and variable coefficients. By using Brouwer fixed point theorem and differential inequality, we obtain some sufficient conditions for the existence and global exponential stability of periodic solutions for a class of fuzzy BAM neural networks with distributed delays and variable coefficients. In addition, we also present an illustrative example to show the effectiveness of obtained results.

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