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研究生:林典賡
研究生(外文):TIEN- KENG LIN
論文名稱:建構以類免疫演算法為基礎之空間性與時間性獨立成份分析
論文名稱(外文):A New Spatiotemporal Independent Component Analysis Based on Artificial Immune System
指導教授:邱志洲邱志洲引用關係高淩菁高淩菁引用關係
口試委員:呂奇傑蔡榮發
口試日期:2012-06-21
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:經營管理系碩士班
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:34
中文關鍵詞:空間性及時間性獨立成份分析類免疫系統
外文關鍵詞:Spatiotemporal Independent Component Analysis algorithmArtificial Immune System
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在瞬息萬變的生活之中,到處都充斥著各種形形色色的訊號。但這些觀察到的訊號中,往往包含許多雜訊,因此如何在不受雜訊的干擾下,從觀察到的資訊中分離出原始訊號是許多研究重視的問題。因此,本研究利用空間性及時間性獨立成份分析(Spatiotemporal independent component analysis algorithm,ST-ICA)去分離混合訊號,但所分離的結果不是很明顯。故我們使用類免疫演算法(Artificial Immune System, AIS)來結合空間性及時間性獨立成份分析(AIS-STICA)去分離混合訊號。而AIS-STICA所分離出結果明顯優於STICA。為了證明本研究所提之方法的分離結果是正確的,本研究利用指標評比峰值訊號雜訊比(Peak signal to noise ratio, PSNR)做驗證,此結果也顯示AIS-STICA優於STICA。總結以上,本研究成功地以AIS-STICA改善STICA所面臨的問題。

There are various signals, such as audio signal and images. These signals usually contain many noises. So, how to separate the noises from signals is an important issue. In order to properly address this problem, spatiotemporal independent component analysis is adopted in this research. However, the traditional spatiotemporal independent component analysis (STICA) may not provide a promising result. Therefore, this research proposes an AIS-STICA approach to signal processing. The results showed that the separation capability of the AIS-STICA is better than that of the traditional STICA.

中文摘要...............................................i
英文摘要...............................................ii
誌謝...................................................iii
目錄...................................................iv
表目錄.................................................v
圖目錄.................................................vi
第一章 緒論............................................1
1.1 研究背景......................................1
1.2 研究目的......................................3
1.3 研究架構......................................3
第二章 文獻探討........................................5
2.1獨立成分分析...................................5
2.2空間性及時間性獨立成份分析.....................7
2.3類免疫演算法...................................8
第三章 研究方法........................................10
3.1獨立成分分析...................................11
3.2空間性及時間性獨立成份分析.....................14
3.3類免疫演算法...................................16
3.3.1 類免疫演算法之特性......................16
3.3.2 類免疫反應..............................17
3.4 AIS-STICA.....................................19
第四章 研究結果........................................21
4.1資料來源.......................................21
4.2評估效能指標...................................22
4.3估計結果.......................................23
4.4參數比較.......................................25
第五章 結論與建議......................................29
參考文獻.............................................30
表目錄
表3.1免疫系統對應類免疫求解問題之關係..................18
表4.1合成訊號公式......................................21
表4.2合成資料用的混合矩陣..............................22
表4.3演算法結果........................................23
表4.4迭代數為10的PSNR值................................25
表4.5迭代數為50的PSNR值................................26
表4.6迭代數為100的PSNR值...............................27
圖目錄
圖1.1訊號處理方法架構圖................................1
圖1.2研究架構圖........................................4
圖3.1研究流程圖........................................10
圖3.2FastICA流程圖.....................................13
圖3.3空間性及時間性獨立成份分析流程圖..................16
圖3.4類免疫演算法流程圖................................18
圖3.5AIS-STICA的流程圖.................................20
圖4.1原始訊號圖形......................................21
圖4.2混合訊號圖形......................................22
圖4.3STICA分離結果.....................................24
圖4.4 AIS-STICA 分離結果...............................24
圖4.5迭代數為10的PSNR值................................26
圖4.6 迭代數為50的PSNR值...............................27
圖4.7迭代數為100的PSNR值...............................28


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