多層のパーセプトンと直感的な曖昧なZ番号を使用してTERの割り当てのための新しいフレームワーク
1Publicity Department, Changsha Normal University, Changsha, Hunan, 410100, China. yangtt821124@163.com.
Scientific reports
|August 26, 2025
まとめ
この研究は,直感的な曖昧なZ数とニューラルネットワークを使用して,従業員のパフォーマンス評価とリソースの割り当てのための新しいモデルを導入します. 不確実または不完全な従業員データを効果的に処理することで 人材管理を強化します.
科学分野:
- 組織心理学とデータサイエンスです
- 人材管理と人工知能
背景:
- 従来の従業員評価とリソース割り当て方法は 不確実で複雑なパフォーマンスデータと戦っています
- 効果的な訓練と教育資源 (TER) の配分は,労働力の能力と開発に不可欠です.
研究 の 目的:
- 正確な従業員の業績評価とリソースの割り当てのための新しいモデルを提案する.
- データの不確実性と不正確さを管理する従来の方法の限界に対処する.
主な方法:
- パフォーマンスデータの不確実性を扱うために,直感的な曖昧なZ-Numbersの統合されたアプリケーション.
- 従業員のパフォーマンスの傾向を予測するために多層のパーセプトンネットワークを使用しました.
- メンバーシップ,非メンバーシップ,評価の躊躇性を管理するために曖昧なロジックを使用します.
主要な成果:
- 提案されたモデルは,0.9967のR二乗値で高い予測精度を示した.
- 実験的な分析により,従業員のリソース配分が著しく改善されたことが確認されました.
- 従業員の不確実性や不完全なパフォーマンスのデータを 効果的に処理します
結論:
- 新しいモデルは従来の従業員評価と割り当ての枠組みに比べて 大きく進歩しています
- 組織的な人材管理とパフォーマンス最適化のための柔軟でスケーラブルなソリューションを提供します.
- データの希少性を減らし,リアルタイムの統合を可能にするために,さらなる研究が推奨されています.
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