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研究生:周建興
研究生(外文):Chien-Hsing Chou
論文名稱:學習機制之研究及其應用
論文名稱(外文):The Development of Learning Mechanisms and Their Applications
指導教授:賴友仁蘇木春蘇木春引用關係
指導教授(外文):Eugene LaiMu-Chun Su
學位類別:博士
校院名稱:淡江大學
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:2003
畢業學年度:91
語文別:英文
論文頁數:164
中文關鍵詞:類神經網路群聚分析臉孔偵測自所組織特徵映射圖增強式學習學習機制分類者系統群聚演算法
外文關鍵詞:Neural NetworksCluster AnalysisFace DetectionSOM Feature MapsReinforcement LearningLearning MechanismClassifier SystemsClustering Algorithm
相關次數:
  • 被引用被引用:3
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  • 下載下載:39
  • 收藏至我的研究室書目清單書目收藏:1
類神經網路是一種能有效解決問題的工具,除了能應用於未知環境的問題上,其最重要的特點就是具備有學習的能力。類神經網路可以在與環境的互動中,藉由學習的機制來改善此類神經網路的效能。學習機制(亦可被稱做學習演算法)最重要的目的就是調整類神經網路中神經元的鍵結值以及其他參數,以提昇此網路的效能,進而達成某些既定的目標。一般而言,學習機制可以約略地分為三種不同的架構: (a)監督式學習、(b)非監督式學習以及(c)增強式學習。每種學習機制都有其一定的考量與限制。在這本論文中,作者分別針對此三種學習機制提出新的學習策略或嘗試擴展其應用的層面。
在擴展類神經網路的應用層面上,作者提出一個以自聯想記憶為基礎的臉孔偵測系統。相較於其他臉孔偵測方法,最大的不同點就是作者能以十分簡單易懂的方式,藉由自聯想記憶的類神經網路建構一套可行的臉孔偵測系統。
基本上,群聚演算法也可以被視為一種非監督式的學習機制。因此,本論文的第二個主軸就是探討有關群聚分析中的問題。首先,作者提出一個以對稱距離為基礎之Fuzzy C-means演算法則,此演算法對於幾何特性不同的群聚能適切地加以分群。此外,作者也基於上述的想法,以對稱距離為基礎建立一套群聚驗證的方法,而此群聚驗證的方法亦能夠成功地針對幾何特性不同的群聚,估測出正確的群聚個數。除此之外,針對群聚資料於空間中分佈密度不均勻時,亦或當群聚大小差異很大的情況下,作者也提出另一種可行的群聚驗證方法,而且此群聚驗證的方法,還可被應用在處理影像壓縮的問題上。近年來,有許多研究者成功地將自我組織特徵映射圖應用於群聚分析的問題上;然而這些研究方法的成功與否,往往取決於自我組織特徵映射圖是否能夠呈現良好的拓撲關係。因此,作者提出一種新的方法來量測自我組織特徵映射圖的拓撲關係。如果經過量測的自我組織特徵映射圖之拓撲關係並沒有保持的很好,我們將進一步利用一個治療機制來修補此自我組織特徵映射圖,以藉此獲得一個有良好拓撲關係的自我組織特徵映射圖。
最後作者提出一個以模糊分類者為基礎之增強式學習演算法則,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。
Neural Networks are analytic systems that address problems whose solutions have not been explicitly formulated. The most appealing property of a neural network is the ability of the network to learn from its environment, and to improve its performance through learning. Learning mechanism is referred to as a procedure for modifying the weights and the corresponding parameters of a network to perform some tasks. Generally speaking, learning mechanisms can be roughly divided into three broad categories: (a) supervised learning, (b) unsupervised learning, and (c) reinforcement learning. For the aforementioned learning mechanisms, each learning mechanism has its own considerations, constraints, and limitations. In this dissertation, several new learning algorithms are developed for each learning mechanism and their applications are explored.
The first issue of this dissertation is to explore the possibility of applying associative memories for locating frontal views of human faces in complex scenes. Compared with other face detection systems, one of the most appealing properties of this system is that the training task of associative memories can be easily accomplished.
Clustering algorithms can be regarded as unsupervised learning algorithms. The second issue of this dissertation focuses on solving two most serious problems in cluster analysis. A modified version of Fuzzy C-means algorithm using the point symmetry distance is first proposed in this dissertation. The proposed modified version of Fuzzy C-means algorithm can deal with clusters with totally different geometrical properties on which many other clustering algorithms cannot perform well. Then a new cluster validity measure which adopts the same idea of "point symmetry” is also proposed in the dissertation. The new validity measure can be applied in estimating the number of clusters of different geometrical structures. In addition to the point-symmetry-based measure, another new validity measure is also proposed to deal with clusters with different densities and/or sizes. The new validity measure can be applied to reduce the edge degradation in vector quantization of image compression. Recently, several different approaches are proposed to apply self-organizing feature maps in cluster analysis. The success of these approaches fully depends on whether feature maps can be topologically ordered. Therefore, a novel measure is first proposed to check whether a feature map is topologically ordered. Then a healing mechanism is proposed to repair a feature map which is not topologically ordered.
The last issue of this dissertation is on the topic of reinforcement learning. A new approach to fuzzify classifier systems is proposed and then applied to generate a neuro-fuzzy system. During the training procedure, the neuro-fuzzy system can incrementally construct its architecture and tune the system parameters without the need of desired outputs.
Contents
1 Introduction
1.1 Introduction of Neural Networks…………………………………………………………1
1.2 Face Detection Based on Associative Memory…………………………………………..4
1.3 Cluster Analysis…………………………………………………………………………..5
1.3.1 Similarity Measure………………………………………………………………..6
1.3.2 Cluster Validity……………………………………………………………………..7
1.4 Self-Organizing Feature Maps……………………………………………………………8
1.5 Reinforcement Learning………………………………………………………………….9
1.6 Organization of This Dissertation………………………………………………………..9
2 Associative-Memory-Based Human Face Detection
2.1 Survey of Related Works……………………………………………………………….11
2.2 System Overview………………………………………………………………………13
2.3 Training Associative Memories………………………………………………………..14
2.3.1 Background…………………………………………………………………….15
2.3.2 Multiple Associative Memories………………………………………………..16
2.3.3 Generating and Selecting Training Examples…………………………….……19
2.3.4 Improvements…………………………………………………………………..21
2.4 Experimental Results…………………………………………………………………...22
2.5 Conclusions…………………………………………………………………………….24
3 Fuzzy C-Means Algorithm with the Point Symmetry Distance
3.1 Survey of Related Works………………………………………………………………..25
3.2 The Point Symmetry Distance and Its Limitations……………………………………...27
3.3 The Proposed Clustering Algorithm………………………………………………….....33
3.3.1 The Fuzzy C-Means Algorithm…………………………………………………33
3.3.2 The PSFCM Algorithm…………………………………………………………34
3.4 Experimental Results…………………………………………………………………...38
3.5 Discussions and Conclusions…………………………………………………………...47
4 A New Measure of Cluster Validity Using Point Symmetry
4.1 Survey of Related Works………………………………………………………………..50
4.2 Cluster Validity Measures………………………………………………………………51
4.3 The Proposed Validity Measure Using Point Symmetry………………………………..55
4.4 Experimental Results……………………………………………………………………60
4.5 Discussions and Conclusions………………………………………………………...…64
5 A New Cluster Validity Measure and Its Application to Image Compression
5.1 Motivation………………………………………………………………………………66
5.2 The Proposed Cluster Validity Measure………………………………………………...70
5.3 Experimental Results……………………………………………………………………73
5.4 Application to Image Compression……………………………………………………..77
5.4.1 Measure for Codebooks…………………………………………………………79
5.4.2 Modified K-Means Algorithm…………………………………………………..80
5.4.3 Image Compression……………………………………………………………..84
5.5 Discussions and Conclusions…………………………………………………………...89
6 A Healing Mechanism to Improve the Topological Preserving Property of Feature Maps
6.1 Survey of Related Works………………………………………………………………..90
6.2 The Proposed Quantitative Measure…………………………………………………….92
6.3 Simulation Results of the Proposed Quantitative Measure……………………………..96
6.4 The Healing Mechanism………………………………………………………………...99
6.5 Simulation Results of the Healing Mechanism………………………………………..105
6.6 Conclusions……………………………………………………………………………111
7 A New Approach to Fuzzy Classifier Systems and its Application in Self-Generating Neuro-Fuzzy Systems
7.1. Survey of Related Works……………………………………………………………...113
7.2 Brief Review of Classifier Systems……………………………………………………115
7.3 A New Approach to Fuzzy Classifier Systems………………………………………...118
7.3.1 The Architecture of the Proposed Neuro-Fuzzy Systems……………………..119
7.3.2 Construction of an ACSNFIS…………………………………………………122
7.4 Updating Parameters…………………………………………………………………..124
7.4.1 Updating Rules for the Strengths………………………………………………124
7.4.2 Updating Rules for the Antecedents…………………………………………...127
7.4.3 Updating Rules for Consequents………………………………………………130
7.5 Experimental Results…………………………………………………………………..130
7.5.1 The Inverted Pendulum Problem………………………………………………131
7.5.2 The Problem of Back-Driving a Truck………………………………………...134
7.6 Application to Color Quantization………………………………………......................137
7.6.1 The Construction of the ACSNFIS……………………………….....................139
7.6.2 Definition of the Evaluation Function………………………………................140
7.6.3 Experimental Results………………………………..........................................142
7.7 Conclusions………………………………....................................................................144
8 Conclusions and Future Works………………………………...........................146
References………………………………......................…………………………...................149
Publications………………………………......................………………………………........163
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