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研究生:潘德鴻
研究生(外文):PHAM, DUC-HUNG
論文名稱:使用仿腦神經網路控制器的混沌系統同步化控制加密和解密安全通信
論文名稱(外文):Encryption and Decryption of Secure Communication using Chaotic System Synchronization Control with Brain-Imitated Neural Network Controller
指導教授:林 志 民林 志 民
指導教授(外文):CHIH-MIN LINCHIH-MIN LIN
口試委員:林 志 民陳德育莊季高龔宗鈞王文俊
口試委員(外文):CHIH-MIN LINTE-YU CHENJIH-GAU JUANGCHUNG-CHUNG KUNGWEN-JUNE WANG
口試日期:2022-07-06
學位類別:博士
校院名稱:元智大學
系所名稱:電機工程學系甲組
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:2022
畢業學年度:110
語文別:英文
論文頁數:116
中文關鍵詞:圖像加密圖像解密安全通訊渾沌系統同步化TSK型模糊系統小波Type-2模糊系統大腦情感控制器小腦模型控制器
外文關鍵詞:Image encryptionimage decryptionsecure communicationchaotic system synchronizationTSK fuzzy systemwavelet type-2 fuzzy systembrain imitated neural network controllercerebellar model articulation controller
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渾沌系統有非常特別且吸引人的特性,近年來都一直被拿來深入研究,在安全通訊上,由於混沌系統的自由性與複雜度,主從渾沌系統同步化就是一個好方法,本論文中,實現了基於大腦情感學習控制器(BINN)的混沌系統同步控制並將其應用於安全通信上以提高訊息安全性. 首先使用了遞迴大腦情感小腦模型控制器 (RCFCB) 控制三維 (3-D) 渾沌系統, 接著針對四維 (4-D) 渾沌系統使用遞迴TSK模糊系統結合大腦情感小腦模型控制器(RTFBECAC)進行控制, 最後使用小波Type-2模糊神經網路結合大腦情感控制器(WT2FBINN) 用於五維 (5-D) 渾沌系統 然後, 使用這些方法應用於圖像與聲音訊號加解密以實現通訊安全, 並通過模擬驗證證實所提出之方法的有效性.
Chaos is a remarkably fascinating phenomenon that has been intensively researched in recent decades. For secure communication, chaotic synchronization of master and slave is a good method because chaotic properties are free and complex. In this dissertation, chaotic synchronization based on Brain imitated neural network controller (BINN) is implemented and applied to secure communication to improve information security. Some types of Brain- Imitated Neural Networks are used: Recurrent Fuzzy Brain-Imitated Cerebellar Model Articulation Controller (RCFBC) is used for three-dimensional (3-D) chaotic systems first, then Recurrent Takagi-Sugeno-Kang Fuzzy System combined with Brain- Imitated Cerebellar Model Articulation Controller (RTFBECAC) is used for four-dimensional (4-D) chaotic systems, and finally Wavelet type-2 fuzzy Neural Network combined with Brain Imitated Controller (WT2FBIC) is used for five-dimensional (5-D) chaotic systems. Then, these methods are applied to the encryption and decryption of image and audio signals for secure communication. The simulation test can confirm the efficiency of the proposed methods.
Title Page i
Letter of Approval ii
Letter of Authority iii
Abstract in Chinese v
Abstract in English vi
Acknowledgment viii
Table of Contents x
List of Tables xiv
List of Figures xv
Nomenclature xix
Chapter 1: Introduction 1
1.1 General Remark and Introduction of the Study 1
1.2 Objectives and Structure of the Dissertation 3
Chapter 2: Problem Formulation of Chaotic Synchronization System and Its Application to Secure Communication 5
Chapter 3: Synchronization of Chaotic System Using a Recurrent Brain-Imitated Cerebellar Model Articulation Controller and Its Applications for Image Secure Communications 8
3.1 Introduction 8
3.2 Problem Formulation 10
3.3 Brain Imitated Neural Network Controller System Design 10
3.3.1 Input domain: 11
3.3.2 Recurrent Association Memory Domains (M1 and M2): 12
3.3.3 Receptive field domain of RCMAC (R): 12
3.4 Online Learning Laws and Convergent Analysis 14
3.5. Numerical Simulation Examples 19
3.5.1 Synchronization of 3D Genesio chaotic system 19
3.5.2 Image secure communication using 3D chaotic synchronization 24
3.5.2.1 Histogram 30
3.5.2.2 Correlation between two adjacent pixels 31
3.5.2.3 Information entropy 34
3.5.2.4 Differential attack analysis 35
3.6 Summary 36
Chapter 4: Audio Secure Communications Using 4D Chaotic System Synchronization Control with TSK Fuzzy Brain Imitated Neural Netwrok Controllers 37
4.1 Introduction 37
4.2 Problem Formulation 40
4.3 Recurrent TSK Fuzzy Brain Imitated Cerebellar Model Articulation Controller System Design 42
4.4 Numerical Simulations for Audio Signal Secure Communication 54
4.4.1 Synchronization of a 4D hyper-jerk chaotic system 54
4.4.2 Application of audio secure communication 61
4.4.2.1 Description of audio secure communication system 61
4.4.2.2 Signal to noise ratio, peak signal to noise ratio, and correlation coefficient 64
4.5 Summary 65
Chapter 5: Secure Communication of Medical Image Via 5D Chaotic System Synchronization using a Wavelet Type-2 Fuzzy Brain-Imitated Neural Network 67
5.1 Introduction 67
5.2 Problem formulation 69
5.3 Wavelet Type-2 Fuzzy Brain Imitated Learning Controller (WT2FBINN) 72
5.3.1 Structure of WT2FBINN 72
5.3.2 Online Learning Parameters of WT2FBINN 75
5.3.3 Modified grey wolf optimizer (MGWO) 77
5.4 Simulation Results 79
5.4.1 Synchronization of a chaotic 5D Lorentz system 79
5.4.2 Medical image secure transmission 83
5.4.2.1 Histogram analysis 84
5.4.2.2 Entropy 86
5.4.2.3 Different attack analysis 87
5.4.2.4 Peak signal to noise ratio (PSNR) 89
5.4.2.5 Cropping attack analysis 89
5.4.2.6 NIST SP800-22 test 91
5.5 Summary 92
Chapter 6: Conclusions and Suggestions for Future Research 93
6.1 Conclusions 93
6.2 Suggestions for Future Research 94
References 95
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