四元数
人工神经网络
数学
指数稳定性
离散时间和连续时间
不动点定理
动力系统理论
微分方程
应用数学
固定点
指数函数
控制理论(社会学)
纯数学
数学分析
计算机科学
非线性系统
量子力学
统计
机器学习
物理
人工智能
几何学
控制(管理)
作者
Yongkun Li,Xiaofang Meng
出处
期刊:Complexity
[Hindawi Publishing Corporation]
日期:2017-01-01
卷期号:2017: 1-15
被引量:48
摘要
We propose a class of neutral type quaternion-valued neural networks with delays in the leakage term on time scales that can unify the discrete-time and the continuous-time neural networks. In order to avoid the difficulty brought by the noncommutativity of quaternion multiplication, we first decompose the quaternion-valued system into four real-valued systems. Then, by applying the exponential dichotomic theory of linear dynamic equations on time scales, Banach’s fixed point theorem, the theory of calculus on time scales, and inequality techniques, we obtain some sufficient conditions on the existence and global exponential stability of pseudo almost periodic solutions for this class of neural networks. Our results are completely new even for both the case of the neural networks governed by differential equations and the case of the neural networks governed by difference equations and show that, under a simple condition, the continuous-time quaternion-valued network and its corresponding discrete-time quaternion-valued network have the same dynamical behavior for the pseudo almost periodicity. Finally, a numerical example is given to illustrate the feasibility of our results.
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