使用進化演算法的模糊化類神經網路等化器

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姓名

李建民(Chien-Min Lee ) 查詢紙本館藏

畢業系所

電機工程研究所

論文名稱

使用進化演算法的模糊化類神經網路等化器

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摘要(中)

傳統上，等化器的設計非常簡單，但通常僅能處理線性判別區域的信號空間。本論文將介紹結合模糊理論與類神經網路架構的等化器，解決等化器無法處理非線性判別區域的的問題。模糊系統的優點是不需要精確的數學模型，另一方面結合人類的知識於系統的設計上。模糊化的好處是可以提供更佳的推廣性、錯誤容忍度、以及更適合應用於真實世界中的非線性系統。而類神經網路的架構，其複雜度可分割非線性判別區域。論文中並提出一種進化演算法則（Evolutionary Algorithms, EAs），應用於模糊化類神經網路等化器上，進化演算法則是一種隨機最佳化(stochastic optimization)的技術，模仿生物遺傳機制的基因進化概念而來，屬於一種多點平行式的全域搜尋（global search）法則。文中將以模擬的方式比較使用進化演算法與傳統演算法，對於模糊化類神經網路等化器效能表現的優劣。

關鍵字(中)

★ 模糊化類神經網路
★ 符元間干擾
★ 進化演算法
★ 類神經網路

關鍵字(英)

★ EAs
★ ISI
★ neural network
★ neuro-fuzzy network

論文目次

目　　錄
第一章　緒論..................................................1
1.1 前言....................................................1
1.2 符元間干擾..............................................2
1.3 等化器..................................................3
第一章類神經網路...........................................5
2.1 建構類神經元模型..........................................6
2.2 判別迴授等化器............................................7
2.3 最佳判別界限.............................................10
2.4 多層感知器...............................................13
第二章模糊化類神經網路....................................17
　3.1 模糊系統...............................................17
3.2 模糊化類神經網路.........................................18
3.3 模糊化類神經等化器.......................................21
第三章學習演算法..........................................24
4.1梯度坡降演算法............................................25
4.1.1 BPN learning algorithm for DFE........................25
4.1.2 BPN learning algorithm for MLP-based equalizer.........26
4.1.3 BPN learning algorithm for neuro-fuzzy equalizer.......33
4.2 進化演算法...............................................39
4.2.1 個體的描述 Individual..................................42
4.2.2 評估 Evaluation function...............................42
4.2.3 交配 Crossover.........................................43
4.2.4 突變 Mutation..........................................44
4.2.5 子代選擇 Offspring Selection...........................45
4.2.6 族群 Population........................................45
4.2.8 風險分析 Risk analysis.................................45
第四章模擬結果 Simulation results.........................47
5.1 收斂特性 Convergence characteristics.....................47
5.1.1 適存度 Fitness.........................................49
5.1.2 均方差 Mean square error...............................53
5.2 判別區間 Decision region.................................60
5.3 位元錯誤率 Bit error rate performance....................66
結論.........................................................76
參考文獻
圖目、表目
圖1.1　檢波程序所產生的符號間干擾.............................3
圖1.2　適應性等化器運作流程圖.................................4
圖1.3　(a).發射端傳送之二元信號 (b).受雜訊干擾之信號 (c).經等化器恢復之信號................................................4
圖2.1　類神經元模型...........................................6
圖2.2　活化函數...............................................6
圖2.3　感知器之架構方塊.......................................7
圖2.4　判別迴授等化器的基本架構圖.............................7
圖2.5　兩個輸入的感知器再不同數目的隱藏層時對判別區域的表現...9
圖2.6　通道在信號雜訊比(signal noise ratio, SNR)為20dB時，不同延遲情況下之均方差(Mean Square Error, MSE).....................11
圖2.7　不同雜訊強度下，通道之最佳判別區域圖..................12
圖2.8　類神經網路的前向傳遞模式..............................13
圖2.9　設計類神經網路的步驟與流程............................14
圖2.10　類神經網路等化器.....................................15
圖3.1　模糊化類神經網路的架構................................19
圖3.2　模糊化類神經等化器的架構..............................21
圖4.1　類神經網路的迴授傳遞模式..............................27
圖4.2　不同的p值下與的關係.................................32
圖4.3　進化演算法流程圖......................................41
圖4.4　進化演算法虛擬程式碼..................................41
圖5.1　Population learning curve for neuro-fuzzy model-based on linguistic model（fuzzy rule=3）when population size=(200,200)....................................................49
圖5.2　Population learning curve for neuro-fuzzy model-based on linguistic model（fuzzy rule=6）when population size=(200,200)....................................................50
圖5.3　Population learning curve for neuro-fuzzy model-based on linguistic model（fuzzy rule=9）when population size=(200,200)....................................................51
圖5.4　Population learning curve for neuro-fuzzy model-based on linguistic model（fuzzy rule=12）when population size=(200,200)....................................................52
圖5.5　Simulation results showing relative convergence rate performance for DFE and MLP structure........................53
圖5.6　Neuro-fuzzy model-based on linguistic model simulation results showing relative convergence rate performance for different fuzzy rules at SNR = 20dB..........................54
圖5.7　Simulation results showing relative convergence rate performance by (4,1)DFE with (9,3,1)MLP structure for different population sizes at SNR = 20dB...............................55
圖5.8　Simulation results showing relative convergence rate performance by neuro-fuzzy model-based on linguistic model for different population sizes at SNR = 20dB.....................55
圖5.9　EA搜尋完畢後進入fine turning 程序.....................56
圖5.10　Convergence rate performance for BPN and EAs (search miss) for (4,1)DFE with (9,3,1)MLP structure.................57
圖5.11　Convergence rate performance for BPN and EAs (search success) for (4,1)DFE with (9,3,1)MLP structure..............58
圖5.12　Convergence rate performance for BPN and EAs (search success) for neuro-fuzzy equalizer-based on linguistic model....................................................59
圖5.13　Decision region formed by (2,0)DFE with (9,3,1)MLP structure....................................................61
圖5.14　Decision region formed by (2,0)DFE with (9,3,1)MLP structure after 300 generations by Eas.......................62
圖5.15　Decision region formed by neuro-fuzzy equalizer-based on linguistic model (fuzzy rule=6) after 5000 samples........63
圖5.16　Decision region formed by neuro-fuzzy equalizer-based on linguistic model (fuzzy rule=12) after 5000 samples.......64
圖5.17　Decision region formed by neuro-fuzzy equalizer-based on linguistic model (Fuzzy rule=6) after 300 generations by Eas....................................................65
圖5.18　Performance of (4,1)DFE with (9,3,1)MLP structure, training by BPN learning algorithm...........................66
圖5.19　BER of (4,1)DFE structure, training by LMS,..........67
圖5.20　BER of (4,1)DFE with (9,3,1)MLP structure, training by EAs, population size = (200,200).............................68
圖5.21　BER of (4,1)DFE with (9,3,1)MLP structure, for different learning algorithms................................69
圖5.22　BER of (4,1)DFE with (9,3,1)MLP structure training by EAs with and without fine turning............................70
圖5.23　BER of neuro-fuzzy equalizer-based on linguistic model for different learning algorithms............................71
圖5.24　BER of neuro-fuzzy equalizer-based on linguistic model for different fuzzy rule, population size=(200,200)..........72
圖5.25　BER of neuro-fuzzy equalizer-based on linguistic model for different fuzzy rule, population size=(200,200)..........73
圖5.26　BER of Neuro-Fuzzy Equalizer-based on Linguistic Model with and without fine turning................................74
表2.1　通道之無雜訊座標......................................10

參考文獻

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指導教授

賀嘉律(Chia-Lu Ho)

審核日期

2001-6-28

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