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 摘要 IAbstract II致謝 IV目錄 V圖目錄 VII表目錄 IX縮寫索引 X符號索引 XI第1章 緒論 11.1 研究動機 11.2 論文貢獻 11.3 章節概要 2第2章 文獻探討與背景介紹 32.1 原始數據 32.1.1 最小相移鍵控(Minimum Shift Keying, MSK) 42.1.2 跳頻展頻(Frequency Hopping Spread Spectrum , FHSS) 52.1.3 訊息格式 52.2 預處理 72.2.1 特徵提取(Feature Extraction) 72.2.2 特徵縮放(Feature Scaling) 112.2.3 特徵選擇(Feature Selection) 122.3 機器學習 132.3.1 模型選擇(Model Selection) 142.3.2 交叉驗證(Cross-Validation) 242.3.3 超參數最佳化(Hyperparameter Optimization) 262.4 模型評估 272.4.1 學習曲線(Learning Curve)和驗證曲線(Validation Curve) 272.4.2 混淆矩陣(Confusion Matrix) 28第3章 基於機器學習的起始點檢測方法 303.1 基於機器學習的起始點檢測方法架構 30第4章 模擬與分析結果 364.1 分類器演算法效能分析 364.1.1 特徵選擇分析 364.1.2 模型選擇分析 374.2 整體演算法效能分析 434.2.1 校正偏差方法分析 444.2.2 提出訊號起始點檢測方法分析 44第5章 結論與未來方向 47參考文獻 49
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