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研究生:林子翔
研究生(外文):Tzu-Hsiang Lin
論文名稱:自適應粒子群體最佳演算法結合細胞神經網路應用於灰階影像雜訊消除
論文名稱(外文):Adaptive Particle Swarm Optimization Based on Cellular Neural Network for Noise Cancellation of Gray Image
指導教授:蘇德仁蘇德仁引用關係
指導教授(外文):Te-Jen Su
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:97
語文別:英文
論文頁數:78
中文關鍵詞:細胞神經網路自適應粒子群最佳演算法灰階影像雜訊消除
外文關鍵詞:Cellular Neural NetworkAdaptive Particle Swarm OptimizationGray Image Noise Cancellation
相關次數:
  • 被引用被引用:1
  • 點閱點閱:163
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  • 下載下載:4
  • 收藏至我的研究室書目清單書目收藏:0
本篇論文旨在使用自適應粒子群體最佳演算法(adaptive particle swarm optimization, APSO)探討細胞神經網路(cellular neural network, CNN)中的離散細胞神經網路系統控制。由於APSO慣性權重適當的選擇給予整體和局部之間搜尋的平衡性,研究顯示較大的權重能幫助增加收斂速度而較小的權重則能精準收斂值。
其研究主題包括輸入控制與迴授模板參數設計的最佳化及討論離散細胞神經網路在灰階影像雜訊消除上的應用。基於自適應粒子群最佳演算法,在不需要事先設定離散細胞神經網路控制模板參數的情況下,它會設計輸入模板參數並且得到最佳的模板參數,將之用來消除受到雜訊污染的灰階影像。最後本研究將獲得的結果提出實際的例子來驗證我們所提出來的方法能有效的應用於灰階影像雜訊消除。
In this thesis, the control of discrete time cellular neural network (DTCNN) systems via adaptive particle swarm optimization (APSO) approach is presented. Due to proper selection of inertia weight of APSO gives balance between global and local searching, the research shows larger weight helps to increase the convergence speed while smaller one benefits the convergence accuracy.
A novel method for designing templates of discrete time cellular neural networks for gray image noise cancellation is developed. Based on APSO method, this approach can automatically update the parameters of the templates of discrete time cellular neural network to optimize them for diminish noise interference in polluted image. Finally, the demonstrated examples are presented to illustrate the effectiveness of the proposed APSO-CNN methodology.
Contents
Abstract in Chinese I
Abstract II
Acknowledgement III
Contents IV
List of Figures VI
List of Tables VIII

Chapter 1 Introduction 1
1.1 Background and Related Work 1
1.2 Motivation 3
1.3 Overview of the Thesis 4

Chapter 2 Particle Swarm Optimization 6
2.1 Swarm Intelligence in Nature 6
2.2 Evolutionary Computation Techniques 11
2.3 Standard Particle Swam Optimization Technique 13
2.4 Adaptive Particle Swam Optimization Technique 19

Chapter 3 Cellular Neural Network 21
3.1 Introduction 21
3.2 The CNN array and equations 22
3.2.1 Some basic definitions for the CNN 25
3.3 The template operation 30
3.3.1 The feedback template operator 31
3.3.2 The control template operator 32
3.3.3 Non-uniform processor CNN and Multiple Neighborhood Size CNN 33

Chapter 4 Cellular Neural Networks Training by APSO 35
4.1 Design of Cellular Neural Network System 35
4.2 Genetic Algorithm Based on CNN Template Learning 36
4.3 PSO Based on CNN Template Learning 38
4.4 APSO Based on CNN Template Learning 40

Chapter 5 Gray Image Noise Cancellation 43
5.1 Introduction 43
5.2 Examples and Results 44
5.2.1 Example 1 44
5.2.2 Example 2 54

Chapter 6 Conclusions 60

References 62
List of Publications 67
Biography 68
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