隨著物聯網(Internet of Things, IoT)相關技術的快速發展與使用需求的提升，家庭中有網路使用需求的裝置大量增加使得對於網路頻寬(Network Bandwidth)的需求也隨之增加，因此需要一個有效的管理方式確保家庭網路的服務品質(Quality of Service, QoS)。
本論文提出一個基於廣義回歸類神經網路(General Regression Neural Net-work, GRNN)模型家庭網路頻寬管理系統的優化方案，使該系統同時具備快速運算能力、低成本且小體積的嵌入式頻寬管理系統，使其適用於一般家庭的網路使用情境，能夠容易的嵌入現有的家庭網路設備中達到頻寬管理的目的。本系統根據訓練資料預測下一個時間點所需使用的網路頻寬量，並且能夠根據預測結果快速地修正頻寬分配量；此外系統會將最新的頻寬分配結果作為訓練資料加入模型中，藉由不斷地更新訓練資料使系統能夠不斷適應使用模式。
在論文中也討論了GRNN與其他模型在頻寬分配情境下的效果比較藉此說明較適合GRNN做為頻寬預測模型的原因。此外，根據不同的訓練資料量探討本論文所提出的優化硬體電路與其利用管線化運算所帶來的效益。論文最後也討論頻寬管理系統中相關重要參數的調整對整體執行速度的影響。

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