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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
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
Design Example: Underdamped Parallel RLC Circuit01:17

Design Example: Underdamped Parallel RLC Circuit

288
Consider designing an oscillator circuit, a crucial component in various electronic devices and systems. The objective is to create an oscillator circuit with specific characteristics: a damped natural frequency of 4 kHz and a damping factor of 4 radians per second. To accomplish this, a parallel RLC circuit is employed, known for its ability to sustain oscillations at a resonant frequency. In this case, the damping factor is pivotal in achieving the desired performance.
Starting with a fixed...
288
Linear time-invariant Systems01:23

Linear time-invariant Systems

254
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
254
Oscillations In An LC Circuit01:30

Oscillations In An LC Circuit

2.3K
An idealized LC circuit of zero resistance can oscillate without any source of emf by shifting the energy stored in the circuit between the electric and magnetic fields. In such an LC circuit, if the capacitor contains a charge q before the switch is closed, then all the energy of the circuit is initially stored in the electric field of the capacitor. This energy is given by
2.3K
RLC Circuit as a Damped Oscillator01:30

RLC Circuit as a Damped Oscillator

973
An RLC circuit combines a resistor, inductor, and capacitor, connected in a series or parallel combination.
Consider a series RLC circuit. Here, the presence of resistance in the circuit leads to energy loss due to joule heating in the resistance. Therefore, the total electromagnetic energy in the circuit is no longer constant and decreases with time. Since the magnitude of charge, current, and potential difference continuously decreases, their oscillations are said to be damped. This is...
973

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相关实验视频

Updated: Jun 29, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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非线性振荡器网络的数据高效推断.

Bharat Singhal1, Minh Vu1, Shen Zeng1

  • 1Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.

IFAC-PapersOnLine
|March 26, 2024
PubMed
概括

本研究介绍了一种数据效率高的网络推理方法,它结合了相关统计和模型拟合. 它可靠地从有限的数据中解码网络结构,优于现有技术.

科学领域:

  • 复杂的系统复杂的系统.
  • 网络科学 网络科学
  • 非线性动力学是一种非线性动力学.

背景情况:

  • 从测量数据中推断网络对于理解和控制复杂系统至关重要.
  • 现有的数据驱动方法通常需要大量的测量数据,这往往是不切实际的.
  • 非线性振荡器网络是各种科学领域的常见模型.

研究的目的:

  • 为非线性振荡器网络开发一个数据效率高的网络推断技术.
  • 为了应对网络结构解码中有限的测量数据的挑战.
  • 为识别网络连接提供可靠的方法.

主要方法:

  • 开发了一种新的方法,将相关统计与模型拟合程序相结合.
  • 该方法在斯图尔特-兰多振荡器网络上进行了测试.
  • 验证使用昼夜基因表达模型和实验Rössler电子振荡器网络数据进行.

主要成果:

  • 建议的数据效率方法可靠地识别网络结构,即使测量数据有限.
  • 该技术与现有的网络推断方法相比,表现出更高的性能.
  • 跨多种系统的成功应用,包括生物和电子振荡器.
关键词:
数据驱动的建模数据驱动的建模网络推理 网络推理非线性振荡器 不线性振荡器时间序列分析 时间序列分析

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结论:

  • 开发的数据效率高的网络推断技术为数据稀疏的复杂系统提供了强大的解决方案.
  • 这种方法提高了网络分析在实际场景中的可行性.
  • 这些发现对理解和控制各种非线性动态网络具有重要意义.