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相关概念视频

Controller Configurations01:22

Controller Configurations

128
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
132
Multimachine Stability01:25

Multimachine Stability

198
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
198
PI Controller: Design01:24

PI Controller: Design

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Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

282
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Neural Circuits01:25

Neural Circuits

1.3K
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

Updated: Jul 25, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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基于内存状态反控制的延迟冲动隐性混合神经网络的主-奴隶同步.

Zekun Wang1, Guangming Zhuang1, Xiangpeng Xie2

  • 1School of Mathematical Sciences, Liaocheng University, Liaocheng Shandong 252059, PR China.

Neural networks : the official journal of the International Neural Network Society
|June 23, 2023
PubMed
概括

这项研究通过使用记忆状态反控制实现了对延迟冲动神经网络的H∞主-奴隶同步. 该方法确保了系统的稳定性和性能,即使有时间变化的延迟.

关键词:
主奴隶同步的同步延迟的冲动神经网络 延迟的冲动神经网络自由权重矩阵方法.隐含/单一的马尔科夫跳跃系统记忆状态反控制的反控制

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相关实验视频

Last Updated: Jul 25, 2025

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科学领域:

  • 控制理论 控制理论
  • 神经网络的神经网络的神经网络
  • 系统工程 系统工程

背景情况:

  • 主-奴隶同步对于复杂的系统至关重要.
  • 延迟的冲动性混合神经网络带来了重要的控制挑战.
  • 确保在不确定性下的H∞性能是关键要求.

研究的目的:

  • 为了研究延迟冲动隐性混合神经网络的H∞主-奴隶同步.
  • 开发一个强大的记忆状态反控制策略.
  • 为了实现保证的性能指数和系统可接受性.

主要方法:

  • 一个随机的冲动时间依赖的Lyapunov-Krasovskii函数的开发.
  • 记忆状态反控制的应用.
  • 在控制器设计中使用线性矩阵不等式 (LMI).
  • 采用自由权重矩阵技术来处理时间变化的延迟.

主要成果:

  • 闭环系统实现了随机可接受性和规定的H∞性能.
  • 一个模式依赖的内存状态反同步控制器成功设计.
  • 该方法有效地放松了对时间变化延迟的导数的约束.
  • 在遗传调节网络上的模拟结果验证了该方法.

结论:

  • 拟议的内存状态反控制有效地解决了针对目标神经网络的H∞主-奴隶同步问题.
  • 开发的Lyapunov-Krasovskii功能和控制设计技术为类似的复杂系统提供了通用的框架.
  • 这些发现在生物经济系统和其他需要同步复杂动态的领域都有潜在的应用.