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相关概念视频

State Space Representation01:27

State Space Representation

200
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
200
Transfer Function to State Space01:23

Transfer Function to State Space

223
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
223
State Space to Transfer Function01:21

State Space to Transfer Function

196
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
196
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Classification of Systems-II01:31

Classification of Systems-II

138
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
138
Neural Circuits01:25

Neural Circuits

1.1K
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...
1.1K

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相关实验视频

Updated: Jun 19, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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基于时空转换的神经网络,具有可解释结构,用于模拟分布式参数系统.

Peng Wei, Han-Xiong Li

    IEEE transactions on neural networks and learning systems
    |July 25, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种新的时空网络,用于模拟非线性分布式参数系统 (DPS),而无需先前的过程知识. 该方法使用神经网络和高斯过程回归来实现准确,可解释和空间连续的建模.

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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
    11:18

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    科学领域:

    • 控制系统工程 控制系统工程
    • 计算机建模 计算建模
    • 机器学习 机器学习

    背景情况:

    • 工业过程通常以分布式参数系统 (DPS) 的形式建模,由部分微分方程 (PDEs) 控制.
    • 传统的线性建模方法与许多DPS固有的非线性作斗争.
    • 存在对数据驱动的建模技术的需求,这些技术不需要先前的流程知识.

    研究的目的:

    • 提出一种新的时空网络,用于模拟非线性分布式参数系统 (DPS).
    • 开发一种方法,实现空间连续建模,并提供可靠的输出范围.
    • 证明拟议方法在复杂工业过程中的有效性.

    主要方法:

    • 提出了一个时空网络,包含非线性时空分离.
    • 这个问题在正交约束下转化为拉格朗日双优化问题.
    • 连续的空间基础函数 (SBF) 通过空间构造方法来导出.
    • 非线性时间动态是使用高斯过程回归 (GPR) 建模的.

    主要成果:

    • 拟议的神经网络有效地解决了衍生优化问题,提供结构性可解释性.
    • 该方法通过整合空间构造和GPR实现空间连续建模.
    • 为模拟过程提供了可靠的输出范围和置信级别.
    • 对催化反应和电池热过程的实验验证证证了该方法的优越性.

    结论:

    • 开发的时空网络为非线性DPS建模提供了有效的,无知识的方法.
    • 空间结构和GPR的整合使得准确和可解释的建模与可靠的不确定性量化成为可能.
    • 该方法在复杂的工业过程建模和控制中显示出显著的应用前景.