预先分配的时间同步复杂值的记忆神经网络与反应-扩散条款和马尔科夫参数
Hongliang Liu1, Jun Cheng2, Jinde Cao3
1School of Mathematics and Physics, University of South China, Hengyang, 421001, PR China.
概括
这项研究使用新的控制策略实现了复杂的记忆神经网络的预分配时间同步. 该研究确保在用户定义的时间框架内同步,并提供对结算时间的精确控制.
科学领域:
- 具有复杂价值的神经网络.
- 非线性控制系统非线性控制系统
- 动态系统理论 动态系统理论
背景情况:
- 记忆神经网络 (MNN) 对于先进的计算至关重要.
- 在MNN中与反应-扩散项实现同步是具有挑战性的.
- 现有的同步方法往往缺乏精确控制定位时间.
研究的目的:
- 为具有复杂价值的MNN开发预分配时间稳定的控制策略.
- 设计控制器,保证在用户指定的时间内实现同步.
- 解决结算时间规范和激活功能约束方面的局限性.
主要方法:
- 设计两个不同的预分配时间控制器,具有不同的功率指数.
- 应用格林公式和边界条件来处理对称性.
- 对具有马尔科夫参数和反应-扩散项的复杂值的MNN进行同步分析.
主要成果:
- 复杂值的MNNs的同步是在预先分配的时间内实现的.
- 拟议的控制策略允许预先指定结算时间.
- 该方法即使在激活函数和潜在对称性损失的松散约束的情况下也是有效的.
结论:
- 该研究成功地证明了复杂值的MNN的预分配时间同步.
- 拟议的控制器在设定同步截止日期方面提供了更高的灵活性.
- 这些发现促进了复杂动态系统的控制理论的发展.
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