| 研究生: |
楊盛松 cheng-song yang |
|---|---|
| 論文名稱: |
複數訊號多層感知決策回授等化器-使用進化演算法 Multilayer perceptron decision feedback equalizer in complex signals based on evolutionary algorithms |
| 指導教授: |
賀嘉律
Chia-Lu Ho |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 電機工程學系 Department of Electrical Engineering |
| 畢業學年度: | 90 |
| 語文別: | 中文 |
| 論文頁數: | 74 |
| 相關次數: | 點閱:9 下載:0 |
| 分享至: |
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論文提要及內容:
由於在通訊通道中所傳送的訊號主要為複數訊號,而且自然界的通訊通道都是非理想的,這將使接收端所收到的訊號有所失真,此失真源自通道中的雜訊(Noise)及符元干擾(ISI)效應。使用決策回授等化器(DFE)可將此種失真降低。因此,我們想利用類神經網路(Neural)的架構配合最小均方演算法(LMS)、倒傳遞演算法(BP)及進化演算法(EAs)來實踐決策回授等化器處理複數訊號,進而比較各種演算法的性能。在本論文中,我們尤其強調進化演算法的優秀性能,其在處理實數訊號方面已有一些論文提過[1,2],但在處理複數訊號的過程中,我們改善了進化演算法,所以在本論文中,我們也將實數訊號的處理含括在內。而且為了改善進化演算法的耗時缺點,我們也結合了EAs及BP,這種利用EAs求得最佳的一組權數(Weights)並以BP來調適此權數,在決策回授等化器處理訊號上將更為有用。除了各種演算法的介紹外,為了論文的完整性及一致性,本論文將從基本的等化器及類神經網路來開始討論,最後再將電腦模擬的結果附上以說明各種演算法的性能比較。
參考文獻
(Reference)
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