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研究生:徐永吉
研究生(外文):Yong-Ji Xu
論文名稱:教導式及增強式進化學習用於遞迴式小波類神經模糊網路及其應用
論文名稱(外文):Supervised and Reinforcement Evolution Learning for Recurrent Wavelet Neuro-Fuzzy Networks and Its Applications
指導教授:林正堅林正堅引用關係
指導教授(外文):Cheng-Jian Lin
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:資訊工程系碩士班
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:2005
畢業學年度:93
語文別:英文
論文頁數:127
中文關鍵詞:小波類神經網路TSK模糊架構遞迴性網路控制進化學習增強式學習
外文關鍵詞:evolution learningcontrol.Recurrent networkwavelet neural networksTSK-type fuzzy modelreinforcement learning
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本文主要提出教導式及增強式進化學習演算法用於遞迴式小波類神經模糊網路,在本文中遞迴式小波類神經模糊網路是以小波為基底之類神經模糊網路本身為多層網路架構,並整合了傳統TSK 模糊模組及小波類神經網路。而遞迴特性主要是源自於歸屬函數中激發程度,作為內部輸送的變數值,回傳給本身(歸屬函數)做為輸入。在學習演算方面包括教導式進化學習及增強式進化學習。在教導式學習方面,我們提出了動態共生進化與自我建構進化演算法。在動態共生進化演算法主要是提出產生較佳的初始族群以及找出較佳的突變點。而在自我建構進化演算法中我們使用一個子族群來評估取局部解以及多個子族群架構一個遞迴式小波類神經模糊網路的族群架構來取代動態共生進化演算法的族群架構。此外我們也提出了自我架構學習演算法根據輸入資料自動化架構遞迴式小波類神經模糊網路。在自我建構進化演算法的參數學習部分則很類似動態共生進化演算法的學習方式。雖然動態共生進化演算法和自我建構進化演算法在實驗結果中有很好的表現,但這兩個演算法是屬於教導式學習的應用。然而在真實世界中有些資料卻是很昂貴或是很難去取得的。為了解決這樣的問題,我們提出了一個增強式動態共生進化演算法。在增強式動態共生進化演算法中主要是利用一個紀錄錯誤發生時次數的機制作為設計適應函數的方法。在參數學習方面是使用動態共生進化演算法的學習方式。在實驗結果中我們可以看出本文所提出的教導式和增強式進化學習的效能。
In this thesis, supervised and reinforcement evolution learning methods are proposed for recurrent wavelet neuro-fuzzy networks (RWNFN). The RWNFN model is a feedforward multi-layer network which integrates traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). The recurrent property comes from feeding the internal variables, derived from membership function matched degree, back to itself. In the learning algorithm, this thesis proposed supervised and reinforcement evolution learning methods. The supervised evolution learning methods consist of the dynamic symbiotic evolution (DSE) and the self-constructing evolution algorithm (SCEA). In the DSE, the better chromosomes will be initially generated while the better mutation points will be determined for performing dynamic mutation. In the SCEA, we modified the structure of population in the DSE that we use a subpopulation to evaluate a partial solution locally and several subpopulations to construct a full solution. Moreover, the proposed SCEA uses the self-constructing learning algorithm to construct the RWNFN model automatically that is based on the input training data to decide the input partition. The DSE is using to carry out parameter learning of the RWNFN model in the SECA. Although the DSE and SCEA can obtain good performance in the simulations, however in some real-world applications exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolution learning method called the reinforcement dynamic symbiotic evolution (R-DSE) is proposed. In the R-DSE, we formulate a number of time steps before failure occurs as the fitness function. The DSE is used as a way to perform parameter learning. In the simulations, efficiency of the proposed supervised and reinforcement learning methods are verified from these results.
Abstract in Chinese IV
Abstract in English VI
Contents X
List of Figures XII
List of Tables XVIII
Chapter I : INTRODUCTION 1
1.1 Motivation 1
1.2 Literature Review 4
1.3 Thesis Organization 10
Chapter II : A RECURRENT WAVELET NEURO-FUZZY NETWORKS (RWNFN) 11
2.1 Introduction 11
2.2 Wavelet Bases and Wavelet Neural Networks 13
2.3 Structure of the RWNFN Model 16
Chapter III : THE SUPERVISED EVOLUTION LEARNING METHODS
21
3.1 Introduction 21
3.2 Dynamic Symbiotic Evolution 23
3.2.1 The DSE Method 23
3.2.2 Illustrative Example 36
3.3 Self-Constructing Evolution Algorithm 60
3.3.1 The SCEA Method 60
3.3.2 Illustrative Example 71
3.4 Discussion 82
Chapter IV : THE REINFORCEMENT EVOLUTION LEARNING METHOD 84
4.1 Introduction 84
4.2 The Reinforcement Dynamic Symbiotic Evolution 85
4.2.1 The R-DSE Method 85
4.2.2 Illustrative Example 88
4.3 Discussion 112
Chapter V : CONCLUSION 113
Bibliography 115
Vita 125
Publication List 126
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