Fuzzy Cellular Neural Networks

2001 ◽  
pp. 179-196
Author(s):  
Luigi Fortuna ◽  
Gianguido Rizzotto ◽  
Mario Lavorgna ◽  
Giuseppe Nunnari ◽  
M. Gabriella Xibilia ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Fuzzy Cellular Neural Networks

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 Synchronization of Chaotic Delayed Fuzzy Neural Networks under Impulsive and Stochastic Perturbations

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Li ◽  
Qiankun Song
Keyword(s):  
Neural Networks ◽  
Cellular Neural Networks ◽  
Fuzzy Neural Networks ◽  
Itô’S Formula ◽  
Mixed Delays ◽  
Fuzzy Cellular Neural Networks ◽  
Stochastic Perturbations ◽  
Synchronization Problem ◽  
Ito’S Formula ◽  
Fuzzy Neural

The synchronization problem of chaotic fuzzy cellular neural networks with mixed delays is investigated. By an impulsive integrodifferential inequality and the Itô's formula, some sufficient criteria to synchronize the networks under both impulsive and stochastic perturbations are obtained. The example and simulations are given to demonstrate the efficiency and advantages of the proposed results.

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