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研究生:張奉宇
研究生(外文):Feng-Yu Chang
論文名稱:新穎區間第二型模糊類神經系統之設計與應用
論文名稱(外文):A Novel Interval Type-2 Fuzzy Neural System: Design and Its Applications
指導教授:李慶鴻
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
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:2011
畢業學年度:99
語文別:英文
論文頁數:98
中文關鍵詞:區間第二型模糊類神經系統非對稱歸屬函數不確定性邊界同步擾動隨機近似演算法李亞普諾夫定理即時控制字元辨識
外文關鍵詞:interval type-2 fuzzy neural systemasymmetric fuzzy membership functionuncertainty boundssimultaneous perturbation stochastic approximation algorithmLyapunov theoremon-line controloptical character recognition
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本論文提出兩個新穎區間第二型模糊類神經系統,分別為IT2RFNS-A (Interval type-2 recurrent fuzzy neural system with asymmetric membership function)以及IT2TFNS (interval type-2 TSK fuzzy neural system) ,兩系統均使用區間第二型三角非對稱模糊歸屬函數以增強其性能及降低計算量;透過設計之穩定同步擾動隨機近似演算法(stable simultaneous perturbation stochastic approximation algorithm)訓練網路,並應用於非線性系統。
在IT2RFNS-A中,我們加上內嵌的遞迴層使其具備動態的特性。然而,IT2RFNS-A的降階運算缺乏對於不確定性的量測。因此,在本論文提出的IT2TFNS使用不確定性邊界(uncertainty bound)設計降階運算層,進而降低計算複雜度。而做為訓練類神經系統的穩定同步擾動隨機近似演算法是利用目標函數值去組合梯度資訊的一種演算法,如此可省去計算梯度的困擾。同時,本文使用李亞普諾夫穩定性理論設計參數更新法則,除了保證系統之穩定以及效率外,亦提供最佳學習步伐之選擇。此外,我們也改善穩定同步擾動隨機近似演算法無法完成即時控制之缺點,透過順滑平面(sliding surface)機制設計一適應性即時控制器。本文所提出之方法將應用於非線性系統鑑別與即時控制驗證系統之可行性及效能。最後,我們設計一基於IT2TFNS之字元辨識系統及其數位信號處理(DSP)硬體實現。


In this thesis, we propose two novel interval type-2 fuzzy neural systems: interval type-2 recurrent fuzzy neural system with asymmetric membership function (IT2RFNS-A) and interval type-2 TSK fuzzy neural system (IT2TFNS), and their training scheme via stable simultaneous perturbation stochastic approximation (SPSA) algorithm. Herein, we adopt interval type-2 triangular asymmetric fuzzy membership functions (IT2 triangular AFMFs) for the proposed two interval type-2 fuzzy neural systems to improve the performance and efficiency.
In IT2RFNS-A system, an embedded feedback layer is attached to extend the abilities to include dynamic problems. In addition, the type-reduction operation of IT2RFNS-A cannot provide the measurement of uncertainty. Therefore, the IT2TFNS is introduced to reduce computational cost which utilizes of the uncertainty bounds for the type-reduction operation. For training these two novel interval type-2 fuzzy neural systems, we propose a stable SPSA algorithm that only the measurements of objective function are needed to form the gradient information. Meanwhile, we employ the Lyapunov stability analysis to derive a time-variant optimal learning step length for guaranteeing the stability of the system and ensuring the efficient training. In addition, we also develop an on-line control method by using the proposed stable SPSA algorithm. Finally, we propose a fuzzy logic-based optical character recognition system using IT2TFNS and its digital-signal-processing (DSP) hardware implementation. Several simulations including nonlinear system identification and on-line control, and experimental result are done to illustrate the feasibility and the effectiveness of the proposed method.


Contents

Abstract in Chinese i
Abstract in English ii
Acknowledgements in Chinese iv
Contents v
List of Figures viii
List of Tables x

CHAPTER 1. Introduction 1
CHAPTER 2. Preliminaries 5
2.1 Interval Type-2 TSK Fuzzy Logic System 5
2.2 Construction of Interval Type-2 Triangular Asymmetric Fuzzy Membership Function 8
Example 2.1 Comparison of Computational Complexity between IT2 Triangular AFMF and IT2 Gaussian AFMF 11
2.3 Uncertainty Bounds for Type-reduction Operations 14
2.4 Simultaneous Perturbation Stochastic Approximation Algorithm 16
CHAPTER 3. Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear System Control and Identification via Stable SPSA Algorithm 18
3.1 Network Structure of IT2RFNS-A 20
3.2 Training IT2RFNS-A by Stable SPSA Algorithm 23
3.3 Stability Analysis 26
3.4 Simulation Results 31
Example 3.1 Nonlinear System Identification of Chaotic System 31
Example 3.2 Nonlinear System Control of Chua''s Chaotic Circuit 37
3.5 Summary 46
CHAPTER 4. Interval Type-2 Takagi-Sugeno-Kang Fuzzy Neural System via Uncertainty Bounds and Its Application on Nonlinear System Control 47
4.1 Network Structure of IT2TFNS 49
Example 4.1 Comparison of Computational Complexity between Type-reduction by Using Uncertainty Bounds and KM Algorithm 52
4.2 Stable SPSA Algorithm-based On-line Adaptive Controller Design Using IT2TFNS 53
4.2.1 Problem Formulation 54
4.2.2 Design of the On-line Controller 55
4.2.3 Stability Analysis 56
4.3 Simulation Result 59
Example 4.2 Nonlinear System Control of Chua''s Chaotic Circuit 59
4.4 Summary 65
CHAPTER 5. Fuzzy Logic-based Optical Character Recognition System Using IT2TFNS 66
5.1 Optical Character Recognition System 66
5.1.1 Forming Database 67
5.1.2 Training Classifier 73
5.1.3 Classifying 75
5.2 Hardware Implementation 75
5.2.1 Core Chip 76
5.2.2 Programming 79
5.2.3 Optimization 80
5.3 Simulation and Experimental Result 83
Example 5.1 Conparison and Analysis of Normalization Size 83
Example 5.2 Hardware Implementation of the Proposed OCR System 85
5.4 Summary 88
CHAPTER 6. Conclusion and Future Research 89
REFERENCES 91



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