Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Transfer Function in Control Systems01:21

Transfer Function in Control Systems

548
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
548
Block Diagram Reduction01:22

Block Diagram Reduction

236
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
236
State Space to Transfer Function01:21

State Space to Transfer Function

226
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:
226
Transfer Function to State Space01:23

Transfer Function to State Space

287
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...
287
Signal Flow Graphs01:18

Signal Flow Graphs

248
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
248
Network Function of a Circuit01:25

Network Function of a Circuit

312
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
312

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hybrid Nature-Inspired Optimization for the Cell Formation Problem with Machine Reliability and Alternative Routings.

Biomimetics (Basel, Switzerland)·2026
Same author

Metaheuristic-Optimized Convolutional Neural Network for Automated Diagnosis of Viral Pneumonia and Tuberculosis from Chest X-Rays.

Diagnostics (Basel, Switzerland)·2026
Same author

Enhancing Manufacturing Cell Formation Through Availability-Based Optimization Using the Black Widow Optimizer Metaheuristic.

Biomimetics (Basel, Switzerland)·2026
Same author

Evaluating Bio-Inspired Metaheuristics for Dynamic Surgical Scheduling: A Resilient Three-Stage Flow Shop Model Under Stochastic Emergency Arrivals.

Biomimetics (Basel, Switzerland)·2026
Same author

Bioinspired Optimization for Feature Selection in Post-Compliance Risk Prediction.

Biomimetics (Basel, Switzerland)·2026
Same author

A Novel Binary Dream Optimization Algorithm with Data-Driven Repair for the Set Covering Problem.

Biomimetics (Basel, Switzerland)·2026

相关实验视频

Updated: Jul 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K

元启发学的二元化:转移函数真的很重要吗?

José Lemus-Romani1, Broderick Crawford2, Felipe Cisternas-Caneo2

  • 1Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
概括

这项研究引入了一种新的方法,用于使用连续的元启发学来解决二进制组合问题的方法. 研究发现,特定的二进制化规则显著优于传输函数,精英和精英的轮盘规则证明最有效.

关键词:
这就是Q-learning.二元化方案选择选择二元化方案选择多样性指标是多样性的指标.灰狼优化器 灰狼优化器设置覆盖问题的问题.负数与负数共积算法鱼优化算法 鱼优化算法

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

相关实验视频

Last Updated: Jul 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 优化优化 优化优化

背景情况:

  • 二进制组合优化问题在各个领域都很普遍.
  • 连续的元启发学为解决复杂的优化任务提供了一个强大的框架.
  • 有效的二元化对于将连续的元启发学适应于二进制问题至关重要.

研究的目的:

  • 提出和评估一种使用连续元启发学的方法来解决二进制组合问题的方法.
  • 研究不同的二元化方案,包括转移函数和二元化规则,对算法性能的影响.
  • 通过基于强化学习的动作选择来确定二元化的最佳策略.

主要方法:

  • 开发一种基于强化学习的选择器,将转移函数和二元化规则结合起来.
  • 实现和测试各种用于连续元启发的二进制化方案.
  • 对算法性能进行实验分析,重点关注二元化规则对转移函数的影响.
  • 统计测试和图形分析,以评估勘探和开采的权衡.

主要成果:

  • 二元化规则对算法性能产生了比转移函数更大的影响.
  • 一些特定的行动,特别是那些包含精英或精英轮盘二元化规则的行动,产生了更好的结果.
  • 对勘探和开采的分析显示,不同的行动集具有不同的性能特征.
  • 统计测试证实了对二元组合优化特定行动集的优越性.

结论:

  • 提出的方法为选择有效的二元化方案在二元组合优化中提供了一种实用方法.
  • 精英和精英的轮盘二元化规则是强烈推用于提高对二进制问题的连续元启发性能.
  • 进一步的研究可以在这些发现的基础上开发更复杂的二进制化策略并增强优化算法.