Ridge-momentum回帰とグリッドベースの構造最適化を活用した換気ポンプのモデル識別
Cong Toai Truong1,2, Trung Dat Phan1,2, Van Tu Duong1,2
1Key Laboratory of Digital Control and System Engineering (DCSELab), Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
Mathematical biosciences and engineering : MBE
|September 3, 2025
まとめ
この研究では,機械呼吸器のモデルを特定するための新しい方法,運動量で修正されたリッジ回帰 (Ridge-M) が導入されています. Ridge-Mは,呼吸器のパンデミック対策に不可欠な換気ポンプのパラメータを推定する際の安定性と精度を向上させます.
科学分野:
- エンジニアリング
- 制御システム
- 生物医学工学
背景:
- 機械呼吸器は,パンデミック中の呼吸支援に不可欠です.
- 換気装置の正確なモデリングは,システムの不確実性と時間変動のパラメータのために課題を提示します.
- 既存のモデリング技術は 複雑なダイナミクスと 限られた計算リソースで 苦戦しています
研究 の 目的:
- 換気ポンプの自動回帰移動平均 (ARMA) モデルを特定するための新しい強力なアルゴリズムを開発する.
- タイム・バリエーション・システムにおけるモデル識別の安定性と適応性を高める.
- ARMAモデルの注文と時間遅延パラメータの選択を最適化する.
主な方法:
- 運動量で修正されたリッジ回帰の実装 (リッジ-M).
- モデル注文と時間遅延の共同最適化のためのグリッド検索戦略の統合
- Akaike情報基準 (AIC) をモデル構造の選択に使用する.
- ランプと多段階の入力信号を用いた実験的検証.
主要な成果:
- Ridge-Mは,リキュルシブ最小平方 (RLS) と標準のリッジ回帰と比較して,ダイナミックなシステムの行動を捉える上で優れたパフォーマンスを示しました.
- Ridge- Mは,多段階の入力に対して,RLSよりも2. 7%,標準のRidge回帰よりも6. 8%の平均誤差減少を達成しました.
- 標準リッジ回帰は,ランプ入力にわずかな優位性を示したが,リッジ-Mは,最も低い全体的な平均RMSE (31.6236) を示した.
結論:
- 提案されたリッジ-Mアルゴリズムは,機械呼吸器のモデル識別に安定し,適応可能なアプローチを提供します.
- この方法は,騒々しいデータ,時間変動システム,および限られた計算能力を持つ組み込みアプリケーションに適しています.
- この発見は,呼吸器の信頼性と性能を向上させるのに役立ちます.
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