Pseudo Almost Periodic Solutions for SICNNs with Leakage Delays and Complex Deviating Arguments

2015 ◽  
Vol 44 (2) ◽  
pp. 375-386 ◽  
Author(s):  
Qiyuan Zhou
Keyword(s):  
Periodic Solutions ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays ◽  
Deviating Arguments ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic

scholarly journals Pseudo Almost Periodic Solutions for HCNNs with Time-Varying Leakage Delays

2015 ◽  
Vol 1 (1) ◽  
pp. 51-69 ◽  
Author(s):  
Cemil Tunç
Keyword(s):  
Periodic Solutions ◽  
Exponential Dichotomy ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays ◽  
Pseudo Almost Periodic Solutions ◽  
The Fixed Point Theorem ◽  
Pseudo Almost Periodic

Abstract In this paper, we consider a class of high-order cellular neural networks (HCNNs) model with time-varying delays in the leakage terms. We give some sufficient conditions which guarantee the exponential stability of pseudo almost periodic solutions for the model. The obtained results complement with some recent ones in the literature.The technique of proof involves the exponential dichotomy theory and the fixed point theorem. An illustrative example is given with an application.

scholarly journals Global Exponential Stability of Pseudo Almost Periodic Solutions for SICNNs with Time-Varying Leakage Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Wentao Wang ◽  
Bingwen Liu
Keyword(s):  
Exponential Stability ◽  
Periodic Solutions ◽  
Global Exponential Stability ◽  
Functional Method ◽  
Time Varying ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic

This paper is concerned with the shunting inhibitory cellular neural networks (SICNNs) with time-varying delays in the leakage (or forgetting) terms. Under proper conditions, we employ a novel argument to establish a criterion on the global exponential stability of pseudo almost periodic solutions by using Lyapunov functional method and differential inequality techniques. We also provide numerical simulations to support the theoretical result.

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