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研究生:趙春棠
研究生(外文):Chun-Tang Chao
論文名稱:模糊控制以及模糊類神經網路在推廣型卡爾曼濾波器之應用
論文名稱(外文):Fuzzy Control and the Application of Fuzzy Neural System for Extended Kalman Filter
指導教授:鄧清政
指導教授(外文):Ching-Cheng Teng
學位類別:博士
校院名稱:國立交通大學
系所名稱:控制工程系
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:1995
畢業學年度:84
語文別:英文
論文頁數:129
中文關鍵詞:模糊控制模糊類神經網路卡爾曼濾波器PD型控制器
外文關鍵詞:Fuzzy controlFuzzy neural networkKalman filter
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本文對於模糊控制以及模糊類神經系統,做了深入的研究與探討。
在模糊控制方面,我們首先推導模糊控制器與傳統PD (或PI)控制
器之等效性;接著,我們還提出了一個無穩態誤差響應的自調式
PD模糊控制器 另一方面,我們發展了兩個模糊類神經系統:
NFNN 及 FNNS,用以簡化模糊類神經網路的複雜度。此外,我們還
發展出一個結合此二系統特點的合成方法,它能在不需事前的專
家知識情況下,有彈性地鑑別並簡化模糊類神經網路的架構。最
後,我們利用模糊類神經網路建立了一個離散推廣型卡爾曼濾波
器用以估測非線性系統的狀態。
In this thesis we do the work of research about the fuzzy
control and fuzzy-neural systems. In fuzzy control, we first
propose a fuzzy logic controller which is equivalent to the
classical PD (or PI) controller. A PD-like self-tuning fuzzy
controller is then presented that yields zero steady-state
responses. On the other hand, two fuzzy-neural systems, the
NFNN and FNNS, are developed for reducing the complexity of
a fuzzy neural network. Also, a synthesis method combining
the advantages of NFNN and FNNS is explored to flexibly
identify a fuzzy-neural-network structure without prior
expert knowledge. Finally, we construct a discrete extended
Kalman filter by using fuzzy neural networks.
封面
Abstract(Chinese)
Abstract(English)
Acknowledgements(Chinese)
Contents
Figure Captions
Table Captions
1 Introduction and Overview
1.1 Introduction
1.1.1 Motivation
1.1.2 Contribution of this Thesis
1.2 Overview
1.2.1 Organization of this Thesis
1.2.2 Overview
2 Equivalence Between Fuzzy Logic Controllers and PD (or PI) con-trollers
2.1 introduction
2.2 A Special Type PD-Like FLC
2.3 The Effect of the Scaling Factors
2.4 How Can an Flc Equivalent to a PD Controller?
3 A PD- like Self -Tuning Fuzzy Controller Without Steady-State Er-r0r
3.1 Introduction
3.2 the Proposeed STFC
3.2.1 Modified Decision Table
3.2.2 Layered Operation
3.2.3 Supervised Gradient Descent Learning
3.3 Asimple Method for Zeroing Steady-State Error
3.4 Two-Stage Tuning Method
3.5 Simulation Examples
4 Rule Combination in a Normalized Fuzzy Neural Network 4.1 Introduction
4.1 Introduction
4.2 Fuzzy Inference System and the NFNN
4.2.1 The NFNN Structre
4.2.2 Supervised Gradient Descent Learning of the NFNN
4.3 Rule Combination
4.3.1 Definitions
4.3.2 The Rule Combination Theoreem and Algorithm
4.3.3 Minimization Problem in Rule Combination
4.3.4 How to Determine the Consequence Weights with the Same Value?
4.4 All Illustrative Example
5 Simplification of Fuzzy-Neural Systems using Similarity Analysis
5.1 Introduction
5.2 The FNNs Structure and Learning Rules
5.2.1 Layered Operation of the FNNS
5.2.2 Supervised Gradient Descent Learning of the FNNS
5.3 Similarity Measure for Fuzzy Sets and Fuzzy Rules
5.3.1 Similarity Measure for Fuzzy Sets and Fuzzy Rules
5.3.2 Similarity Measure of Fuzzy Rules
5.4 A New On-Line Initialization Method
5.5 An Illustrative Example
6 A Synthesis Method for Identifying the Structure of a Fuzzy Neural Network
6.1 Introduction
6.2 Features in NFNN and FNNS
6.3 Syntnesis by Network Transformation
6.3.1 Proposed Synthesis Method Procedure
6.3.2 Discussions on the Performance of the Proposed Synthesis
6.4 An Illustrative Example
7 A Fuzzy Neural Network Based Extended Kalman Filter
7.1 Introduction
7.2 The Mathematical model and problem statement
7.3 Modeling of the Unkonown Plant
7.4 Modeling Error Compensation
7.5 An Illustrative Example
8 Conclusion and Recommendations
8.1 Conclusion
8.2 Recommendations for Future Research
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