Almost periodic attractor for Cohen–Grossberg neural networks with delay

2009 ◽  
Vol 373 (4) ◽  
pp. 434-440 ◽  
Author(s):  
Zhang Chen ◽  
Donghua Zhao ◽  
Jiong Ruan
Keyword(s):  
Neural Networks ◽  
Almost Periodic ◽  
Periodic Attractor

Dissipativity and periodic attractor for non-autonomous neural networks with time-varying delays

2007 ◽  
Vol 70 (16-18) ◽  
pp. 2953-2958 ◽  
Author(s):  
Yumei Huang ◽  
Daoyi Xu ◽  
Zhichun Yang
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Periodic Attractor

scholarly journals The Existence and Global Exponential Stability of Almost Periodic Solutions for Neutral-Type CNNs on Time Scales

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 321 ◽  
Author(s):  
Bing Li ◽  
Yongkun Li ◽  
Xiaofang Meng
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Periodic Solutions ◽  
Time Scales ◽  
Global Exponential Stability ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Leakage Delays

In this paper, neutral-type competitive neural networks with mixed time-varying delays and leakage delays on time scales are proposed. Based on the contraction fixed-point theorem, some sufficient conditions that are independent of the backwards graininess function of the time scale are obtained for the existence and global exponential stability of almost periodic solutions of neural networks under consideration. The results obtained are brand new, indicating that the continuous time and discrete-time conditions of the network share the same dynamic behavior. Finally, two examples are given to illustrate the validity of the results obtained.

scholarly journals C1-Almost Periodic Solutions of BAM Neural Networks with Time-Varying Delays on Time Scales

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yongkun Li ◽  
Lili Zhao ◽  
Li Yang
Keyword(s):  
Neural Networks ◽  
Periodic Solutions ◽  
Time Scales ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
New Type ◽  
Inequality Techniques

On a new type of almost periodic time scales, a class of BAM neural networks is considered. By employing a fixed point theorem and differential inequality techniques, some sufficient conditions ensuring the existence and global exponential stability ofC1-almost periodic solutions for this class of networks with time-varying delays are established. Two examples are given to show the effectiveness of the proposed method and results.

scholarly journals Global Asymptotic Almost Periodic Synchronization of Clifford-Valued CNNs with Discrete Delays

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yongkun Li ◽  
Jianglian Xiang
Keyword(s):  
Neural Networks ◽  
Existence And Uniqueness ◽  
State Feedback ◽  
Banach Fixed Point Theorem ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Discrete Delays ◽  
Asymptotic Synchronization ◽  
Unital Associative Algebra ◽  
Clifford Numbers

In this paper, we are concerned with Clifford-valued cellular neural networks (CNNs) with discrete delays. Since Clifford algebra is a unital associative algebra and its multiplication is noncommutative, to overcome the difficulty of the noncommutativity of the multiplication of Clifford numbers, we first decompose the considered Clifford-valued neural network into 2m2n real-valued systems. Second, based on the Banach fixed point theorem, we establish the existence and uniqueness of almost periodic solutions of the considered neural networks. Then, by designing a novel state-feedback controller and constructing a proper Lyapunov function, we study the global asymptotic synchronization of the considered neural networks. Finally, a numerical example is presented to show the effectiveness and feasibility of our results.

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