Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Second Order systems I01:20

Second Order systems I

231
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
231
First Order Systems01:21

First Order Systems

162
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
162
Second Order systems II01:18

Second Order systems II

169
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
169
Classification of Systems-I01:26

Classification of Systems-I

293
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
293
Linear time-invariant Systems01:23

Linear time-invariant Systems

398
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...
398
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
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....
131

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

A Systematic Review of Clinical Outcome Trajectories from 3 to 12 Months Following Mild or Moderate Traumatic Brain Injury.

Journal of neurotrauma·2026
Same author

A budget impact analysis of the Dario Diabetes Solution for type 2 diabetes mellitus in a US managed care population.

Journal of medical economics·2025
Same author

Dynamics-aware Representation Learning via Multivariate Time Series Transformers.

... European symposium on artificial neural networks, computational intelligence and machine learning·2025
Same author

Learning Object-Centric Dynamic Modes from Video and Emerging Properties.

Proceedings of machine learning research·2025
Same author

Data-Driven Superstabilization of Linear Systems under Quantization.

Proceedings of the ... American Control Conference. American Control Conference·2025
Same authorSame journal

Identifying the dynamics of interacting objects with applications to scene understanding and video temporal manipulation.

IFAC-PapersOnLine·2025

関連する実験動画

Updated: Sep 8, 2025

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
12:14

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

21.9K

ローナー・フレームワークにおける低注文システムの識別

Arya Honarpisheh1, Rajiv Singh2, Jared Miller3

  • 1ECE Dept., Northeastern University, Boston, MA 02115 USA.

IFAC-PapersOnLine
|August 20, 2025
PubMed
まとめ

この研究では,実験データから低次元のシステムモデルを特定するための新しい方法が導入されています. ローナーベースのアプローチは,より速い単数値の崩壊を提供し,従来のハンケル行列方法と比較してより効率的なモデルを生成します.

キーワード:
バランスのとれた削減ハンケル・マトリックス線形システムローナー・マトリックス低ランク近似サブスペースの方法

さらに関連する動画

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K

関連する実験動画

Last Updated: Sep 8, 2025

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
12:14

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

21.9K
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

6.9K

科学分野:

  • システムエンジニアリング
  • 制御理論
  • 数値分析

背景:

  • 正確なシステム識別は,制御と分析に不可欠です.
  • ハンケルマトリックスベースの識別のような伝統的な方法は,計算が集約され,高次元のモデルを生成します.
  • タイム・ドメインデータによる非パラメトリックの識別は,ユニークな課題を提示します.

研究 の 目的:

  • タイムドメインデータから低次元のシステムモデルを識別するための新しい非パラメトリックの方法を開発する.
  • ローナーベースのインターポレーションと還元の効率を伝統的なハンケル行列法と比較する.
  • 提案されたアプローチの有効性を数値的な例で示す.

主な方法:

  • カラテオドリー・フェジャーとローナーによるインターポレーションを用いてシステムの実現.
  • モデル・オーダー・削減のためのローナー・マトリックス・バランス・リダクション (LBR) ステップを適用する.
  • 単数値の衰退率を分析するためにゾロタレフ数を使用します.

主要な成果:

  • ローナー行列は,システムの痕量基準の有効な推定値として機能する.
  • ロウナー行列の単数値は,ハンケル行列のそれよりも著しく速い衰退率を示しています.
  • Loewnerベースの方法は,比較可能な誤差の限界を持つ低次元のシステムモデルを達成します.

結論:

  • 提案されたローナーベースの方法は,非パラメトリックシステムの識別により効率的なアプローチを提供します.
  • この技術により,精度や計算効率が向上した 縮小型モデルが得られます.
  • この発見は,様々なエンジニアリングのアプリケーションにおけるシステム識別のための貴重な代替案を提供します.