冲动延迟反应-扩散随机神经网络的有限时间限制
IEEE transactions on neural networks and learning systems
|February 22, 2024
概括
这项研究研究了冲动延迟反应-扩散随机神经网络的有限时间局限性. 结果表明,通过图像加密的应用,可以通过控制来维持或实现边界性.
科学领域:
- 计算神经科学是一种计算神经科学.
- 控制理论 控制理论 控制理论
- 应用数学 应用数学 应用数学
背景情况:
- 冲动延迟反应-扩散静态神经网络 (IDRDSNNs) 是具有潜在应用的复杂系统.
- 了解它们的稳定性,特别是有限时间边界性 (FTB) 和有限时间合同边界性 (FTCB),对于可靠的操作至关重要.
- 冲动性扰动可以破坏这些网络的稳定.
研究的目的:
- 为了研究具有混合冲动的冲动延迟反应-扩散静态神经网络 (IDRDSNNs) 的有限时间边界性 (FTB) 和有限时间收缩边界性 (FTCB).
- 制定足够的条件,确保FTB和FTCB.
- 探索实现FTB的方法,即使有冲动性扰动.
主要方法:
- 发展一种新的延迟整体不平等.
- 与新的不平等相结合的比较原则的应用.
- 对FTB和FTCB的足够条件的推导.
主要成果:
- 获得了足够的条件来确保IDRDSNN的有限时间限制 (FTB) 和有限时间合同限制 (FTCB).
- 证明了FTB可以保持,尽管冲动性扰动.
- 显示FTB可以通过冲动控制和优化冲动间隔在不稳定的系统中实现.
结论:
- 提出的方法有效地确保IDRDSNNs的有限时间边界性.
- 冲动控制策略可以稳定受扰动影响的系统.
- 理论结果有实际应用,包括图像加密.
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