基于和功能的连续控制,对竞争神经网络的固定时间同步进行固定时间同步
Caicai Zheng1, Cheng Hu2, Juan Yu2
1College of Mathematics and System Science, Xinjiang University, Urumqi, 830017, China.
概括
本研究介绍了竞争性人工神经网络 (ANN) 中固定时间 (FXT) 同步的连续控制策略,通过整合短期和长期记忆模型来简化分析. 新方法避免了聊天,并提高了同步性能.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 控制理论 控制理论
背景情况:
- 通过不连续控制和对短期记忆 (STM) 和长期记忆 (LTM) 的单独分析,研究了竞争性人工神经网络 (ANN) 的固定时间 (FXT) 同步.
- 传统方法存在复杂的衍生,严格的同步条件,以及由于 signum 函数的喋喋不休而导致的性能降低.
研究的目的:
- 为了应对竞争性ANN在FXT同步中的复杂性和性能降低的挑战.
- 开发新的连续控制方案,以实现FXT同步,提高效率和稳定性.
- 通过将STM和LTM模型集成到一个统一的系统中来减少理论复杂性.
主要方法:
- 通过压缩竞争ANN的STM和LTM模型来建立一个高维系统模型.
- 开发了一个具有切换差异条件的固定时间稳定定理,提供高精度的收时间估计.
- 连续纯功率法控制方案是使用和函数设计的,取代了传统的 signum 函数.
主要成果:
- 拟议的方法通过统一STM和LTM模型来简化理论分析.
- 基于和函数的连续控制方案有效实现FXT同步,避免聊天.
- 同步标准通过数值示例来导出和验证,证明其适用于图像加密.
结论:
- 该研究提出了一种更有效,更不复杂的方法,用于在竞争性ANN中实现固定时间同步.
- 与现有方法相比,开发的连续控制策略提供了更好的性能和稳定性.
- 这些发现在安全图像加密等领域有潜在的应用.
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