对于具有不连续激活的延迟复杂值神经网络的固定/预分配时间同步
1School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, 232001 Anhui People's Republic of China.
Cognitive neurodynamics
|November 18, 2024
概括
这项研究实现了对具有延迟和不连续激活的复杂值神经网络的固定/预分配时间同步,独立于初始状态. 新型控制器在非线性系统中确保更快,更可靠的同步.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 非线性控制理论 不线性控制理论
- 计算神经科学是一种神经科学.
背景情况:
- 非线性系统中的有限时间同步通常取决于初始状态,限制了实际应用.
- 具有时间延迟的复杂值神经网络 (CVNNs) 对于模拟先进的计算系统至关重要.
- 在CVNN中的不连续激活函数对同步分析构成重大挑战.
研究的目的:
- 开发新的控制策略,以在延迟的CVNN中实现固定时间或预分配时间同步.
- 消除同步定位时间对网络初始状态的依赖.
- 将现有的同步结果扩展到具有不连续激活功能的CVNN.
主要方法:
- 为不连续系统量身定制的新型状态反控制器的设计.
- 应用菲利普洛夫规范化技术来处理非平滑动态.
- 利用先进的不平等技术进行稳定性和同步分析.
- 对结算时间的上限的明确估计.
主要成果:
- 建立了实现延迟CVNN与不连续激活的固定/预分配时间同步的新标准.
- 开发的方法克服了对初始状态的依赖,提供了实际的优势.
- 理论结果被证明包含并增强了CVNN与连续激活的现有发现.
- 同步结算时间的上限被明确确定.
结论:
- 拟议的控制方法有效地保证了延迟的CVNN与不连续激活的固定/预分配时间同步.
- 这些发现为复杂的非线性系统中的同步提供了更强大和更实用的框架.
- 数字模拟验证了理论结果,并证明了拟议方法的有效性.
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