The Development of Neural Network Based System Identification ...

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118 CHAPTER 4 NEURAL NETWORK BASED SYSTEM IDENTIFICATION technique that enables us to infer a model that adapts to time-varying dynamics based on real-time data coming from the system. Finally, the selection methods of neural network model structure are also discussed using the Lipschitz coefficient and model validity tests method. The results and discussion of the proposed NN based identification algorithms and architectures are given in the next chapter.
Chapter 5 NN BASED SYSTEM IDENTIFICATION: RESULTS AND DISCUSSION 5.1 INTRODUCTION In this chapter, the model selection and validation results from the proposed NN based system identification methods are presented and discussed. The model selection and validation results from the off-line system identification for various NN architectures are presented in Section 5.2, Section 5.3 and Section 5.4. Section 5.5 provides the comparison results between off-line prediction performance from conventional MLP architecture and the proposed HMLP and modified Elman network. In Section 5.6, the identification results using recursive training are presented. The on-line training proposed in Section 4.3.5 is implemented to identify the attitude dynamics of the UAS helicopter model using the MLP network. The prediction performance of the MLP network trained with off-line LM algorithm and the MLP network trained with repeated recursive Gauss-Newton algorithm are compared. The feasibility of implementing recursive type training is analysed against mini-batch LM algorithm in term of training time. Then, the prediction performance comparison of MLP, HMLP and Elman network using the recursive training method is given in Section 5.7. Finally, the chapter is summarised in Section 5.8.
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The Development of Neural Network B
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ABSTRACT This thesis presents the d
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vii the MLP network, the prediction
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PUBLICATIONS The following is a lis
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xiv TABLE OF CONTENTS CHAPTER 4 CHA
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LIST OF FIGURES 1.1 Examples of typ
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LIST OF FIGURES xix 3.13 Overview o
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LIST OF FIGURES xxi 5.2 The predict
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LIST OF FIGURES xxiii 5.13 The pred
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LIST OF FIGURES xxv 7.1 The locatio
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LIST OF TABLES 3.1 The specificatio
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NOMENCLATURE AFCS Automatic Flight
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LIST OF TABLES xxxi NNARX QP RBF RC
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2 CHAPTER 1 INTRODUCTION Surveillan
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4 CHAPTER 1 INTRODUCTION is then de
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6 CHAPTER 1 INTRODUCTION the dynami
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8 CHAPTER 1 INTRODUCTION models [Sa
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10 CHAPTER 1 INTRODUCTION improved
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12 CHAPTER 1 INTRODUCTION Chapter 3
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Chapter 2 LITERATURE REVIEW The pur
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2.1 INTRODUCTION 17 • Inefficient
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2.1 INTRODUCTION 19 Figure 2.2 The
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2.2 HELICOPTER DYNAMICS MODELLING A
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2.3 NEURAL NETWORK BASED SYSTEM IDE
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2.4 AUTOMATIC FLIGHT CONTROL SYSTEM
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2.5 SUMMARY 53 design based on the
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56 CHAPTER 3 AERIAL PLATFORM AND CU
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168 CHAPTER 6 NEURAL NETWORK BASED
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Chapter 7 FLIGHT CONTROL SYSTEM DES
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7.2 FLIGHT TESTS IMPLEMENTATION 185
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7.3 EXPERIMENTAL RESULTS 187 1 0.8
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7.3 EXPERIMENTAL RESULTS 189 Longit
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7.3 EXPERIMENTAL RESULTS 191 Pitch
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7.3 EXPERIMENTAL RESULTS 193 Flight
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7.3 EXPERIMENTAL RESULTS 195 140 Ya
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7.3 EXPERIMENTAL RESULTS 197 indica
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7.3 EXPERIMENTAL RESULTS 199 20 100
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7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
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7.3 EXPERIMENTAL RESULTS 203 mainta
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7.3 EXPERIMENTAL RESULTS 205 Yaw Ra
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7.4 SUMMARY 207 data measured from
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210 CHAPTER 8 CONCLUSIONS AND FUTUR
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212 CHAPTER 8 CONCLUSIONS AND FUTUR
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214 REFERENCES B. W. Bequette. Non-
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216 REFERENCES Konstantinos Dalamag
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218 REFERENCES Wayne Johnson. Helic
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220 REFERENCES B. Mettler, Mark B.
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222 REFERENCES V. R. Puttige and S.
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224 REFERENCES D.H. Shim. Hierarchi
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226 REFERENCES Nikos Vitzilaios and
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