臺灣博碩士論文加值系統

本論文整合類神經網路系統與遺傳演算法，塔配聲學分析軟體SYSNOISE使用邊界元素法於消音器最佳化設計。
本論文主要分三部份：
(1)干涉型消音器之消音性能：消音器的通路分開為二或更多通路，最後再使其總路會集為一，來減低噪音。並探討尺寸變化，對干涉型消音性能之影響。(2)結合類神經網路系統與遺傳演算法之最佳化方法，並塔配聲學分析軟體SYSNOISE於干涉型消音器之尺寸最佳化設計：利用類神經網路建立其網路模式，再結合遺傳演算法搜尋消音器之最佳化尺寸及聲音傳遞損失值。(3)結合遺傳演算法之最佳化方法，和聲學分析軟體SYSNOISE於干涉型消音器最佳化設計：利用遺傳演算法規劃吸音材料位置，來達到消音性能最佳化。

對於工業界的應用，可有效地減少開發時間和降低生產成本，提升產品競爭力。

The thesis combines Neural Network System and Genetic Algorithm with acoustic analysis software SYSNOISE and which uses Boundary Element Method (BEM) to perform the muffler optimum design.

The thesis is composed of three parts: (1) The performance of noise cancellation muffler: the tube of muffler is separated into two or more, and converge on one tube to reducing noise. Discussing the influence of variation of dimensions on the performance of noise cancellation muffler; (2) Combining Neural Network System and Genetic Algorithm with acoustic analysis software SYSNOISE to the optimum design of dimension of noise cancellation muffler: using Neural Network System to build network system and combining Genetic Algorithm to search the optimum of dimension and transmission loss(TL) of the muffer; (3) Combining Genetic Algorithm with acoustic analysis software SYSNOISE to the optimum design of noise cancellation muffler: using Genetic Algorithm to layout the distribution of absorptive materials to optimized the performance of the muffle.
For the industrial applications, it can save time of design, decrease the production cost, and promote the production competition.

致謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VII
表目錄 XII
第一章 緒論 1
1.1前言 1
1.2文獻回顧 1
1.3研究動機 3
第二章 理論基礎 4
2.1聲音基本理論 4
2.2干涉型消音器 4
2.3類神經網路系統 5
2.3.1多項式類神經網路的概念 5
2.3.2 多項式類神經網路之架構 7
2.3.3 多項式類神經網路之建構 8
2.4遺傳演算法 11
2.4.1 遺傳演算法參數（GA Parameters） 11
2.4.2 遺傳演算法流程 15
第三章 干涉型消音器性能 17
3.1分析設定(Analysis Setting) 18
3.2 d2變化對雙、三彎管干涉型消音器性能的影響 19
3.3 L1變化對雙、三彎管干涉型消音器的影響 22
3.4 L2變化對雙、三彎管干涉型消音器性能的影響 25
3.5 雙、三彎管干涉型消音器性能的比較 28
3.6 實驗數據與模擬分析驗證 29
3.6.1實驗設備 30
3.6.2實驗數據與模擬分析驗證 33
第四章 最佳化尺寸設計於干涉型消音器 34
4.1分析設定(Analysis Setting) 36
第五章 最佳化吸音材料設計於干涉型消音器 43
5.1分析設定(Analysis Setting) 46
5.2遺傳演算法(Genetic Algorithm, GA) 47
5.3最佳化消音器的吸音材料位置 50
5.3.1單彎管之干涉型消音器內部貼附於彎管上的吸音材料最佳化 50
5.3.2雙彎管之干涉型消音器內部貼附於彎管上的吸音材料最佳化 55
5.3.3三彎管之干涉型消音器內部貼附於彎管上的吸音材料最佳化 59
第六章 結論 63
參考文獻 65
附錄一 68
附錄二 73
附錄三 76

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