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姓名	黃得原(De-Yuan Huang)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	適應性模糊化類神經網路及其應用 (The development of an adaptive neuro-fuzzy network and its applications)
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摘要(中)	傳統上建立智慧型機器的途徑有類神經網路及模糊系統。這兩種傳統的方法各有其優缺點及限制。類神經網路的最大優點是：它具有學習的能力，但缺點在於它從許多的範例中所歸納出來的概念，是隱藏在一組網路參數中，但對人來說，這些參數過於抽象無法理解。模糊系統的優點是，它提供一條便捷的路徑，且可善加利用專家處理事物的經驗法則來處理許多工作。更重要的是，它可以提供我們邏輯上的解釋。可是建立模糊系統的瓶頸，在於那些必要的模糊規則要從何而來？因為要建立一個完整且有效的模糊規則庫 (rule base) 是無法單靠人類或是專家口語所給予的經驗法則來建立的。因此，如何整合類神經網路與模糊系統雙方面的優點，近幾年來在相關領域中十分重要的研究課題。 在類神經網路的學習演算法則大致可分為監督式學習、非監督式學習以及增強式學習。在本論文中，提出了一個以模糊多維矩形複合式類神經網路為基礎之增強式學習演算法則 (FHRCNN-Q)。此學習演算法則能在缺乏明確的訓練資料下，自動地建構一個完整的模糊化類神經網路系統，並且能透過增強式學習的方式，自行調整此系統中的參數值來改善此模糊化類神經網路系統的效能。FHRCNN-Q 可以透過增強式學習法則建構其網路架構，並經由增強式學習探索可能的解空間。本論文使用倒單擺系統跟倒車入庫系統來驗證FHRCNN-Q效能。 在一個未知的環境中，移動式機器人的導航系統必須透過感測器讀取環境資訊，並藉由對環境資訊的感知來做出適當的動作。本論文用FHRCNN-Q建構在未知環境中的移動式機器人的導航系統。透過移動式機器人的模擬我們可以看到FHRCNN-Q可經由增強式學習法則建構出移動機器人在未知環境中所需的模糊規則庫。
摘要(英)	Over the last few decades, neural networks and fuzzy systems have established their reputation as alternative approaches to information processing. Both have certain advantages over classical methods, especially when vague data or prior knowledge is involved. However, their applicability suffered from several weaknesses of the individual models. Therefore, combinations of neural networks with fuzzy systems have been proposed, where both models complement each other. A neuro-fuzzy network can be defined as a fuzzy system trained with some algorithm derived from the neural network theory. The integration of neural networks and fuzzy systems aims at the generation of a more robust, efficient and easily interpretable system where the advantages of each model are kept and their possible disadvantages are removed. In this dissertation, the implementation of Q-learning based on fuzzy hyperrectangular composite neural network (FHRCNN) is proposed. The proposed system referred to as FHRCNN-Q. In the proposed FHRCNN-Q, the antecedents part express a model of the environment. In other words, the proposed system construct the uncertain environment by using a FHRCNN model. In the proposed FHRCNN-Q, a fuzzy hyperrectangular composite neural network consists of a set of fuzzy IF-THEN rules that describe the input-output mapping relationship of the networks. Simply stated, the process of presenting input data to each hidden node in a FHRCNN-Q is equivalent to firing a fuzzy rule. The proposed FHRCNN-Q can not only tune its parameters but also incrementally construct its architecture in a reinforcement learning environment. Through injecting new rules into the system, an FHRCNN-Q can explore the possible solution space. The inverted pendulum system and the back-driving truck problem were used to demonstrate the performance of the proposed FHRCNN-Q. It’s important for robots to obtain environmental information by means of their sensory system and decide their own behavior when they move in an unknown environment. This dissertation also presents a FHRCNN-Q approach to a navigation system which allows a goal-directed mobile robot to incrementally adapt to an unknown environment. In the FHRCNN-Q, the fuzzy rules which map current sensory inputs to appropriate actions are built through the reinforcement learning. Some simulation results of a mobile robot illustrate the performance of the proposed navigation system.
關鍵字(中)	★ 增強式學習 ★ Q-learning ★ 模糊化類神經網路 ★ 移動式機器人 ★ 模糊多維矩形複合式類神經網路 (FHRCNN)	關鍵字(英)	★ Q-learning ★ neuro-fuzzy system ★ robot ★ navigation ★ reinforcement learning ★ fuzzy hyperrectangular composite neural network ★ navigation.fuzzy hyperrectangular composite neur
論文目次	ABSTRACT i Acknowledgments v Table of Contents vi List of Figures viii List of Tables ix Nomenclature x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Overview of the Study 3 1.2.1 Fuzzy Hyperrectangular Composite Neural Network 3 1.2.2 Reinforcement Learning algorithms 4 1.3 Organization of Dissertation 5 Chapter 2 The Related Works 6 2.1 Review of Neuro-Fuzzy Systems 6 2.2 Review of the Reinforcement Learning Scheme 10 2.2.1 Brief Review of Q-learning 14 2.2.2 Connectionist Q-learning (QCON) 15 2.2.3 CMAC-based Q-learning 16 2.2.4 Q-Self Organizing Map (Q-KOHON) 17 2.3 Adaptive classifier-system-based neuro-fuzzy inference system 17 2.3.1 The Architecture of the ACSNFIS 18 2.3.2 Learning Mechanisms of the ACSNFIS 19 2.3.3 Updating Weights of the ACSNFIS 21 2.4 Review of Hyperrectangular Composite Neural Network 22 2.4.1 Supervised decision-directed learning (SDDL) algorithm 24 2.5 Discussion 27 Chapter 3 Fuzzy HyperRectangular Composite Neural Networks in a Reinforcement Learning Environment 29 3.1 Fuzzy HyperRectangular Composite Neural Network (FHRCNN) 29 3.1.1 Architecture of Fuzzy HyperRectangular Composite Neural Networks (FHRCNN) 30 3.2 New Approach to Fuzzy HyperRectangular Composite Neural Networks in a Reinforcement Learning Environment 33 3.2.1 The Architecture of the FHRCNN-Q 33 3.2.2 Construction of the FHRCNN-Q 34 3.3 Learning Algorithm of FHRCNN-Q 36 3.4 Updating Parameters of FHRCNN-Q 39 3.3.1 Updating Rules for the Antecedents 41 3.3.2 Updating Rules for the Consequents 42 3.3.3 Updating Q-value for the Rules 43 3.5 Conclusions 43 Chapter 4 The Reinforcement Learning Approach to Control Problems 44 4.1 The Inverted Pendulum Problem 44 4.2 The Back-Driving Truck Problem 49 4.3 A Reinforcement Learning Approach to Robot Navigation 54 4.3.1 The Simulated Robot Navigation System 54 4.3.2 The Proposed FHRCNN-Q Approach to Robot Navigation 55 4.3.3 Experimental Results of Robot Navigation 56 4.4 Conclusions 59 Chapter 5 Conclusions and Future Works 60 5.1 Conclusions 60 5.2 Future Works 61 References 62 Publications 68
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指導教授	蘇木春(Mu-Chun Su)	審核日期	2010-8-4
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