Weighted Pseudo Almost Periodic Solutions for Cellular Neural Networks with Multi-proportional Delays

2018 ◽  
Vol 49 (3) ◽  
pp. 1125-1138 ◽  
Author(s):  
Guangyi Yang ◽  
Wepin Wan
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Cellular Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Proportional Delays ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic

scholarly journals Existence and Global Attractivity of Pseudo Almost Periodic Solutions for Clifford-Valued Fuzzy Neural Networks with Proportional Delays

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3306
Author(s):  
Wen Lv ◽  
Bing Li
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Global Attractivity ◽  
Fuzzy Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Proportional Delays ◽  
Pseudo Almost Periodic Solutions ◽  
Fuzzy Neural ◽  
Pseudo Almost Periodic

In this paper, Clifford-valued fuzzy neural networks with proportional delays, whose leakage term coefficients are also Clifford numbers, are considered. Based on the Banach fixed point theorem and differential inequality technique, we use a direct method to obtain the existence, uniqueness, and global attractivity of pseudo almost periodic solutions for the considered networks. Finally, we provide a numerical example to illustrate the feasibility of our results. Our results are new.

scholarly journals Pseudo almost periodic synchronization of Clifford-valued fuzzy cellular neural networks with time-varying delays on time scales

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yongkun Li ◽  
Shiping Shen
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Time Scales ◽  
Discrete Time ◽  
Cellular Neural Networks ◽  
Fuzzy Cellular Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic

Abstract At present, the research on discrete-time Clifford-valued neural networks is rarely reported. However, the discrete-time neural networks are an important part of the neural network theory. Because the time scale theory can unify the study of discrete- and continuous-time problems, it is not necessary to separately study continuous- and discrete-time systems. Therefore, to simultaneously study the pseudo almost periodic oscillation and synchronization of continuous- and discrete-time Clifford-valued neural networks, in this paper, we consider a class of Clifford-valued fuzzy cellular neural networks on time scales. Based on the theory of calculus on time scales and the contraction fixed point theorem, we first establish the existence of pseudo almost periodic solutions of neural networks. Then, under the condition that the considered network has pseudo almost periodic solutions, by designing a novel state-feedback controller and using reduction to absurdity, we obtain that the drive-response structure of Clifford-valued fuzzy cellular neural networks on time scales with pseudo almost periodic coefficients can realize the global exponential synchronization. Finally, we give a numerical example to illustrate the feasibility of our results.

Attractiveness of pseudo almost periodic solutions for delayed cellular neural networks in the context of measure theory

2021 ◽  
Vol 435 ◽  
pp. 253-263
Author(s):  
David Békollè ◽  
Khalil Ezzinbi ◽  
Samir Fatajou ◽  
Duplex Elvis Houpa Danga ◽  
Fritz Mbounja Béssémè
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Measure Theory ◽  
Cellular Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic

Weighted pseudo almost-periodic solutions of shunting inhibitory cellular neural networks with mixed delays

2016 ◽  
Vol 36 (6) ◽  
pp. 1662-1682 ◽  
Author(s):  
Mohammed Salah M'HAMDI ◽  
Chaouki AOUITI ◽  
Abderrahmane TOUATI ◽  
Adel M. ALIMI ◽  
Vaclav SNASEL
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Cellular Neural Networks ◽  
Mixed Delays ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Pseudo Almost Periodic Solutions ◽  
Pseudo Almost Periodic
Sign in / Sign up

Export Citation Format

Share Document