Memory pattern analysis of cellular neural networks

2005 ◽  
Vol 342 (1-2) ◽  
pp. 114-128 ◽  
Author(s):  
Zhigang Zeng ◽  
De-Shuang Huang ◽  
Zengfu Wang
Keyword(s):  
Neural Networks ◽  
Pattern Analysis ◽  
Cellular Neural Networks ◽  
Memory Pattern

MEMORY PATTERN ANALYSIS OF BAM NEURAL NETWORKS WITH TIME-DELAY

2009 ◽  
Vol 19 (03) ◽  
pp. 843-856 ◽  
Author(s):  
YUNQUAN KE ◽  
CHUN-FANG MIAO
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Pattern Analysis ◽  
Synaptic Connection ◽  
Sufficient Conditions ◽  
External Input ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Exponentially Stable ◽  
Memory Pattern

In this paper, memory patterns of bidirectional associative memory (BAM) neural networks with time-delay are investigated based on stability theory. Several sufficient conditions are obtained such that the equilibrium point is locally exponentially stable when the point is located at the designated position. These conditions, which can be directly derived from the synaptic connection weights and the external input of the BAM neural networks, are very easy to be verified. In addition, three examples are given to show the effectiveness of the results.

New Criteria on Stability of Dynamic Memristor Delayed Cellular Neural Networks

2020 ◽  
pp. 1-13
Author(s):  
Kun Deng ◽  
Song Zhu ◽  
Wei Dai ◽  
Chunyu Yang ◽  
Shiping Wen
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
New Criteria

scholarly journals Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays

2011 ◽  
Vol 21 (4) ◽  
pp. 649-658 ◽  
Author(s):  
Qianhong Zhang ◽  
Lihui Yang ◽  
Daixi Liao
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Exponential Stability ◽  
Differential Inequality ◽  
Sufficient Conditions ◽  
Coincidence Degree ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Inequality Technique

Existence and exponential stability of a periodic solution for fuzzy cellular neural networks with time-varying delays Fuzzy cellular neural networks with time-varying delays are considered. Some sufficient conditions for the existence and exponential stability of periodic solutions are obtained by using the continuation theorem based on the coincidence degree and the differential inequality technique. The sufficient conditions are easy to use in pattern recognition and automatic control. Finally, an example is given to show the feasibility and effectiveness of our methods.

scholarly journals An LMI Approach for Dynamics of Switched Cellular Neural Networks with Mixed Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chuangxia Huang ◽  
Hanfeng Kuang ◽  
Xiaohong Chen ◽  
Fenghua Wen
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Average Dwell Time ◽  
Cellular Neural Networks ◽  
Linear Matrix ◽  
Mixed Delays ◽  
Ultimate Boundedness ◽  
Globally Exponential Stability ◽  
Average Dwell Time Method ◽  
Uniformly Ultimate Boundedness

This paper considers the dynamics of switched cellular neural networks (CNNs) with mixed delays. With the help of the Lyapnnov function combined with the average dwell time method and linear matrix inequalities (LMIs) technique, some novel sufficient conditions on the issue of the uniformly ultimate boundedness, the existence of an attractor, and the globally exponential stability for CNN are given. The provided conditions are expressed in terms of LMI, which can be easily checked by the effective LMI toolbox in Matlab in practice.

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