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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140
Block Diagram Reduction01:22

Block Diagram Reduction

285
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...
285
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K
Multimachine Stability01:25

Multimachine Stability

229
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:
229
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

897
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
897
Elements of Block Diagrams01:25

Elements of Block Diagrams

377
Block diagrams serve as a visual representation of the input-output relationships within a system. An illustrative example is a heating system, where the set temperature activates the furnace to warm the room to the desired level. Block diagrams are versatile, modeling linear systems through Laplace transform variables and nonlinear systems using time domain variables.
A block diagram typically includes essential elements such as comparators, blocks, and feedback loops. Each of these elements...
377

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

CQD-Modified SrTiO<sub>3</sub> for Enhanced Photocatalytic CO<sub>2</sub> Reduction to Methane.

Materials (Basel, Switzerland)·2026
Same author

CaCO<sub>3</sub>/BiO<sub>2-x</sub>/CdS Composite with Rapid Photocatalytic Reduction of Cr(VI) Under Visible Light.

Nanomaterials (Basel, Switzerland)·2026
Same author

Recurrent Stochastic Configuration Networks With Hybrid Regularization for Nonlinear Dynamics Modeling.

IEEE transactions on cybernetics·2026
Same author

Theoretical Advances on Stochastic Configuration Networks.

IEEE transactions on neural networks and learning systems·2025
Same author

Investigation of the quality of life and influencing factors among perimenopausal women.

Archives of gynecology and obstetrics·2025
Same author

MOF-Based Electrocatalysts for Water Electrolysis, Energy Storage, and Sensing: Progress and Insights.

Chemical record (New York, N.Y.)·2025

関連する実験動画

Updated: Sep 10, 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

2.3K

ブロックインクリメントによる回帰ストキャスティック構成ネットワーク

Dianhui Wang1, Gang Dang2

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China.

Neural networks : the official journal of the International Neural Network Society
|August 21, 2025
PubMed
まとめ

ブロック再帰ストキャスティック構成ネットワーク (BRSCN) は,複数のサブリザーバーを追加することで,非線形ダイナミックシステムのモデリングを強化します. このアプローチは,複雑なダイナミクスの学習効率と一般化を改善します.

キーワード:
ブロックインクリメントエコー州所有物持続的な興奮繰り返しストキャスティック構成のネットワークユニバーサル・アプロキシメーション属性

さらに関連する動画

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.2K

関連する実験動画

Last Updated: Sep 10, 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

2.3K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.2K

科学分野:

  • 計算神経科学
  • 機械学習
  • 非線形ダイナミクス

背景:

  • リキュアント・ストキャスティック・コンフィギュレーション・ネットワーク (RSCN) は,順序の不確実性を持つ非線形動的システムに対して有効である.
  • 既存のRSCNは,実装の容易さ,人間の介入の減少,および強力な近似能力を提供しています.

研究 の 目的:

  • 学習能力と効率を向上させるため,ブロック再帰ストキャスティック構成ネットワーク (BRSCN) を導入する.
  • 複雑な非線形動的システムのモデリングを強化します.

主な方法:

  • 複数の貯水槽ノード (サブ貯水槽) を同時に追加できるBRSCNを開発する.
  • 監視メカニズムを使って,それぞれのサブ貯水池を独自の構造で構成します.
  • エコー状態のプロパティを確保するために貯蔵庫のフィードバック行列をスケールします.
  • プロジェクションアルゴリズムによるオンラインの出力重量更新を使用します.
  • パラメータの収束のために持続的興奮条件を確立する.

主要な成果:

  • BRSCNは優れたモデリング効率と学習パフォーマンスを示しています.
  • 提案された方法は,様々なタスクにおける好ましい一般化パフォーマンスを示しています.
  • タイムシリーズ予測,非線形システム識別,産業データ分析で有効性を検証した.

結論:

  • BRSCNは,より効率的な複雑なダイナミクスをモデル化するための大きな可能性を秘めています.
  • 新しいアーキテクチャは,ダイナミック・システム分析のための伝統的なRSCNを改良しています.
  • BRSCNは,難しい非線形モデリングの問題に取り組むための堅固な枠組みを提供します.