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相关概念视频

Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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相关实验视频

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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在混合网络攻击下,针对网络系统的弹性事件触发适应神经网络控制.

Ning Zhao1, Dongke Zhao1, Yongchao Liu2

  • 1College of Control Science and Engineering, Bohai University, Jinzhou 121013, China.

Neural networks : the official journal of the International Neural Network Society
|March 26, 2024
PubMed
概括

本研究介绍了一种新的弹性事件触发机制,用于面临网络攻击的网络控制系统. 该方法使用神经网络来防御未知的攻击,并确保系统稳定性,在机器人操纵器上进行验证.

关键词:
事件触发机制事件触发机制混合网络攻击混合网络攻击.网络控制系统的网络控制系统.神经网络的神经网络的神经网络

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相关实验视频

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科学领域:

  • 控制系统工程 控制系统工程
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 网络控制系统容易受到混合网络攻击,包括拒绝服务和欺骗攻击.
  • 具有恒定值的传统事件触发机制 (ETM) 对于复杂和依赖状态的攻击是不够的.
  • 现有的方法往往难以处理影响系统状态的未知,非线性攻击信号.

研究的目的:

  • 为混合网络攻击下的网络控制系统开发具有弹性事件触发自适应神经网络 (NN) 控制策略.
  • 设计一种能够抵御拒绝服务攻击并节约通信资源的新型ETM.
  • 使用神经网络近似来解决未知依赖状态的非线性欺骗攻击.

主要方法:

  • 提出了一种新的弹性事件触发机制 (ETM),改进了传统的恒值值ETM.
  • 神经网络 (NN) 技术用于近似未知状态依赖的非线性攻击信号.
  • 一个自适应控制器旨在抵消欺骗攻击,通过利亚普诺夫功能分析确保了系统的局限性.

主要成果:

  • 拟议的弹性ETM有效地保护通信资源,同时抵御拒绝服务攻击.
  • 基于NN的方法成功地识别和补偿未知的依赖国家欺骗攻击.
  • 导出了系统局限性的足够条件,并提出了控制增益和事件触发参数的共同设计策略.

结论:

  • 开发的弹性事件触发适应性NN控制策略增强了网络控制系统的安全性和稳定性,以应对混合网络攻击.
  • 该方法在处理未知,依赖于状态的非线性攻击方面表现出有效性,与现有方法相比,这是一个显著的进步.
  • 在机器人操纵系统上的验证证实了拟议的控制策略的实际可行性和性能.