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研究生:陳彥冰
研究生(外文):Yen-Ping Chen
論文名稱:研發自動化類神經網路於動作辨識、未知動態系統鑑別與控制
論文名稱(外文):Development of Automated Neural Networks for Activity Recognition, Unknown Dynamic System Identification and Control
指導教授:王振興王振興引用關係
指導教授(外文):Jeen-Shing Wang
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
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:2008
畢業學年度:96
語文別:英文
論文頁數:155
中文關鍵詞:鑑別與控制遞迴類神經網路動作辨識
外文關鍵詞:Activity recognitionIdentification and ControlRecurrent neural network
相關次數:
  • 被引用被引用:0
  • 點閱點閱:212
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  • 下載下載:59
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出三種結合自動化建構演算法之類神經網路來處理不同的動態應用問題。首先,針對未知動態系統鑑別及控制,我們提出兩個遞迴類神經網路與其自動化建構演算法。自動化建構演算法將維度估測、混合式參數初始化與參數學習演算法整合為一個有系統的程序,可鑑別出最小架構及最佳化類神經網路參數。利用本論文所提出的網路結合其自動化建構演算法可精確地將未知非線性系統鑑別成一個系統維度最小的模型表示式。接著,根據模型表示式,發展一個包含非線性消除器及線性控制器之混合式控制方法來消除未知系統的全域非線性行為後再成功地控制未知系統。最後我們研發一個自動建構演算法來建構模糊基底函數辨識器,使其可利用單一無線三軸加速度計來辨識人類日常動作。此建構演算法是以動態線性識別分析方法的結果為基礎,此方法能在不儲存所有訓練資料與增加及刪減模式的情況下動態地更新散佈矩陣。經由真實人類動作辨識及未知非線性動態系統鑑別與控制的基準測試例子之電腦模擬,我們已經成功地驗證所提出的自動化類神經網路及建構演算法於建構高效能精簡網路的有效性。
This dissertation presents three types of neural networks coupled with automated construction algorithms for tackling different dynamic problems. First, we presented two recurrent networks and their automated construction algorithms for unknown dynamic system identification and control. The automated construction algorithms unify order determination, hybrid parameter initialization, and parameter learning into a systematic procedure for identifying a minimal structure and optimizing the network parameters, respectively. The proposed networks with their automated construction algorithms are capable of accurately identifying unknown nonlinear systems into minimal model representations. Next, according to the model representations, a hybrid control scheme consisting of a nonlinear eliminator and a linear controller was developed to remove the global nonlinear behavior and then to control the unknown systems successfully. Finally, an automated construction algorithm was developed for constructing a fuzzy basis function classifier that is capable of recognizing human daily activities using a wireless tri-axial accelerometer. The construction algorithm is based on the outcome of a dynamic linear discriminant analysis which can dynamically update the scatter matrices in both incremental and decremental modes without storing all training data in memory. Computer simulations on real-world human activity recognition and benchmark examples of unknown nonlinear dynamic system identification and control have successfully validated the effectiveness of the proposed neural networks and automated construction algorithms in constructing parsimonious networks with satisfactory performance.
中 文 摘 要 I
ABSTRACT II
CONTENTS IV
TABLES VII
FIGURES VIII
Chapter 1. Introduction 1
1.1 Motivation and Literature Survey 1
1.2 Dissertation Contributions 7
1.3 Dissertation Organization 9
Chapter 2. Fully Automated Construction Algorithm for Hammerstein Recurrent Neuro-Fuzzy Networks 11
2.1 Introduction 11
2.2 Structure of Hammerstein Recurrent Neuro-Fuzzy Network 14
2.3 Online Fully Automated Construction Algorithm 19
2.3.1 System Order Determination Algorithm 21
2.3.2 Online System Order Determination Algorithm 25
2.3.3 MCA Clustering Algorithm 29
2.3.4 Kaczmarz’s Algorithm 34
2.3.5 Recursive Recurrent Learning Algorithm 35
2.3.6 Summary of Online Fully Automated Construction Algorithm 42
2.4 Simulation Results 43
2.5 Summary 60
Chapter 3. Identification and Hybrid Control for Unknown Nonlinear Dynamic System Using Fully Automated Recurrent Neural Network 61
3.1 Introduction 61
3.2 Structure of Fully Automated Recurrent Neural Network (FARNN) 65
3.3 Fully Automated Construction Algorithms for FARNN 68
3.4 Hybrid Controller Design Based on FARNN 81
3.4.1 Nonlinearity Eliminator (NLE) Construction 85
3.4.2 Linear Controller Design Based on NLE 89
3.5 Simulation Results 90
3.6 Summary 107
Chapter 4. Human Activity Detection Using Automated Fuzzy Basis Function Classifier 109
4.1 Introduction 109
4.2 Linear Discriminant Analysis for Feature Dimension Reduction 112
4.2.1 Dynamic Linear Discrimiant Analysis 113
4.2.2 Example: Iris Data 117
4.3 Automated Fuzzy Basis Function Classifier Construction Based on Dynamic Linear Discriminant Analysis 119
4.3.1 Data Preprocessing 119
4.3.2 Feature Extraction 120
4.3.3 Automated Fuzzy Basis Function Classifier Construction Algorithm 122
4.3.4 Summary of Construction Algorithm for Human Activity Detection 127
4.4 Experimental Design and Results 128
4.5 Summary 134
Chapter 5. Conclusions 135
5.1 Conclusions 135
5.2 Recommendations for Future Work 137
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