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研究生:林家增
研究生(外文):Chia-Tseng Lin
論文名稱:具有類免疫倒傳遞演算法的模糊類神經網路控制器之研究
論文名稱(外文):THE STUDY ON FUZZY NEURAL NETWORK CONTROLLER USING ARTIFICIAL IMMUNE BACK-PROPAGATION ALGORITHM
指導教授:呂虹慶
指導教授(外文):Hung-Ching Lu
學位類別:碩士
校院名稱:大同大學
系所名稱:電機工程學系(所)
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:2010
畢業學年度:99
語文別:英文
論文頁數:100
中文關鍵詞:倒單擺模糊類神經網路倒傳遞演算法類免疫演算法混沌系統
外文關鍵詞:artificial immune algorithminverted pendulumback-propagation algorithmfuzzy neural networkchaotic system
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在這篇論文裡一個具有類免疫倒傳遞演算法的模糊類神經網路控制器被提出且應用在非線性系統上。所提出的控制器是由模糊類神經網路辨證器、類免疫估測器、迫近控制器和計算控制器組成。首先,模糊類神經網路辨證器是用來估測非線性系統的動態。其中辨證器的參數包括權重、標準差跟平均值是經由倒傳遞演算法來調整。接著,藉由類免疫估測器來估測模糊類神經網路辨證器的初值包括權重、標準差跟平均值及倒傳遞演算法的參數;至於類免疫估測器的訓練階段共分成初始化、交配、突變跟演化四個步驟;另外,計算控制器是計算控制力;迫近控制器是用來消除系統的不確定項。最後,藉由倒單擺及二階混沌系統的模擬結果證實了所提出控制器的性能及有效性。
A fuzzy neural network (FNN) identifier based on back-propagation artificial immune (BPIA) algorithm, named the FNN-BPIA controller, is proposed for the nonlinear systems in this thesis. The proposed controller is composed of an FNN identifier, an IA estimator, a hitting controller, and a computation controller. Firstly, The FNN identifier is utilized to estimate the dynamics of the nonlinear system. These parameters which include weights, means, and standard deviations of the FNN identifier are adjusted by the BP algorithm. Secondly, the initial values which include weights, means, and standard deviations of the FNN identifier and the parameters of the BP algorithm are estimated by the IA estimator. Thirdly, the training process of the IA estimator has four stages which include initialization, crossover, mutation, and evolution. Further, the computation controller is given to calculate the control effect and the hitting controller is utilized to eliminate the uncertainties. Finally, the inverted pendulum system and the second-order chaotic system are simulated to verify the performance and the effectiveness of the FNN-BPIA controller.
ACKNOWLEDGEMENTS i
ABSTRACT (IN ENGLISH) ii
ABSTRACT (IN CHINESE) iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
CHAPTER
1 INTRODUCTION 1
2 THE FUNDAMENTAL OF FUZZY NEURAL NETWORK 6
3 DESIGN OF FNN-BPIA CONTROLLER 10
3.1 System Description 10
3.1.1 Description of the Nonlinear System 10
3.1.2 Stability Analysis 12
3.2 The FNN-BPIA Controller Design 14
3.2.1 The Computation Controller 14
3.2.2 Structure Phase 16
3.2.3 Parameter Learning Phase 18
3.3 Summary 35
4 SIMULATION RESULTS 37
4.1 The Inverted Pendulum System 37
4.2 The Second-Order Chaotic System 66
5 CONCLUSION 92
REFERENCES 93
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