相对延迟记忆的固定时间同步复杂值竞争神经网络
1School of Mathematics Science and Institute of Mathematics and Interdisciplinary Sciences, Tianjin Normal University, Tianjin, 300387, China.
概括
本研究使用新标准和控制器实现了对记忆复杂值竞争神经网络 (MCVCNNs) 的固定时间同步 (FXS). 这些方法确保了更快的融合,并应用于图像保护.
科学领域:
- 计算神经科学是一种神经科学.
- 复杂系统动力学 复杂系统动力学
- 控制理论 控制理论
背景情况:
- 记忆神经网络提供了先进的计算能力.
- 复杂值的神经网络对于处理阶段信息至关重要.
- 神经网络中的同步对于信息处理和稳定性至关重要.
研究的目的:
- 研究具有比例延迟的记忆复杂值竞争神经网络 (MCVCNNs) 的固定时间同步 (FXS).
- 制定不那么保守的同步标准.
- 设计有效的控制器来实现FXS和近似结算时间.
主要方法:
- 使用利亚普诺夫方法和不等式技术来得出新的同步标准.
- 新型开关控制器的设计采用复杂数规范,避免分解为实值系统.
- 进行了结算时间 (ST) 的近似计算.
主要成果:
- 为了在MCVCNNs中实现FXS,建立了两个不那么保守的标准.
- 建议的控制器有效地确保在固定的时间内同步.
- 模拟验证了标准的有效性和在图像保护中的实际应用.
结论:
- 开发的标准和控制器为MCVCNNs的固定时间同步提供了显著的进步.
- 该方法为处理复杂值系统提供了更有效的方法.
- 该研究表明了MCVCNNs在安全图像处理应用中的潜力.
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