The Development of Neural Network Based System Identification ...

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218 REFERENCES Wayne Johnson. Helicopter theory. Princeton University Press, Princeton, 1980. Adem Kalinli and Seref Sagiroglu. Elman network with embedded memory for system identification. J. Inf. Sci. Eng., 22(6):1555–1568, 2006. S. M. Kamruzzaman and Md M. Islam. Extraction of symbolic rules from artificial neural networks. WASET Transactions on Science, Engineering and Technology, 10: 271–277, 2010. URL http://arxiv.org/abs/1009.4570. Bekir Karlik and A. Vehbi Olgac. Performance analysis of various activation functions in generalized mlp architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1(4):111–122, 2010. Farid Kendoul. Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. Journal of Field Robotics, 29(2):315–378, 2012. ISSN 1556-4967. A. Khosravi, S. Nahavandi, D. Creighton, and A.F. Atiya. Comprehensive review of neural network-based prediction intervals and new advances. Neural Networks, IEEE Transactions on, 22(9):1341 –1356, Sept. 2011. ISSN 1045-9227. doi: 10.1109/TNN. 2011.2162110. S. K. Kim and D. M. Tilbury. Mathematical modeling and experimental identification of an unmanned helicopter robot with flybar dynamics. J. Robot. Syst., 21(3):95–116, 2004. Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In The 1995 International Joint Conference on Artificial Intelligence, pages 1137–1145, 1995. M. Vijaya Kumar, S. Suresh, S.N. Omkar, Ranjan Ganguli, and Prasad Sampath. A direct adaptive neural command controller design for an unstable helicopter. Engineering Applications of Artificial Intelligence, 22(2):181 – 191, 2009. ISSN 0952- 1976. doi: 10.1016/j.engappai.2008.07.004. URL http://www.sciencedirect.com/ science/article/pii/S0952197608001280. MV Kumar, SN Omkar, R. Ganguli, P. Sampath, and S. Suresh. Identification of helicopter dynamics using recurrent neural networks and flight data. JOURNAL OF THE AMERICAN HELICOPTER SOCIETY, 51(2):164–174, 2006. Rajan Kumar, Ranjan Ganguli, and S.N. Omkar. Rotorcraft parameter estimation using radial basis function neural network. Applied Mathematics and Computation, 216(2):584 – 597, 2010. ISSN 0096-3003. doi: 10.1016/j.amc.2010.01.081. URL http://www.sciencedirect.com/science/article/pii/S0096300310001050. A.T. Kutay, A.J. Calise, M. Idan, and N. Hovakimyan. Experimental results on adaptive output feedback control using a laboratory model helicopter. Control Systems Technology, IEEE Transactions on, 13(2):196 – 202, march 2005. ISSN 1063-6536. doi: 10.1109/TCST.2004.839563. M. Kuure-Kinsey and B.W. Bequette. Improved nonlinear predictive control performance using recurrent neural networks. In American Control Conference, 2008, pages 4197– 4202, 2008. doi: 10.1109/ACC.2008.4587152.
REFERENCES 219 M. Kuure-Kinsey, R. Cutright, and B.W. Bequette. Computationally efficient neural predictive control based on a feedforward architecture. In American Control Conference, 2006, pages 6 pp.–, 2006a. doi: 10.1109/ACC.2006.1657169. M. Kuure-Kinsey, R. Cutright, and BW Bequette. Computationally efficient neural predictive control based on a feedforward architecture. Industrial & Engineering Chemistry Research, 45(25):8575–8582, 2006b. M. Lawrynczuk. A family of model predictive control algorithms with artificial neural networks. International Journal of Applied Mathematics and Computer Science, 17 (2):217–232, 2007a. 288ZN Times Cited:17 Cited References Count:42. M. Lawrynczuk. An efficient nonlinear predictive control algorithm with neural models based on multipoint on-line linearisation. In EUROCON, 2007. The International Conference on ”Computer as a Tool”, pages 777 –784, September 2007b. doi: 10. 1109/EURCON.2007.4400364. Tsungnan Lin, B.G. Horne, P. Tino, and C.L. Giles. Learning long-term dependencies in narx recurrent neural networks. Neural Networks, IEEE Transactions on, 7(6): 1329 –1338, Nov 1996. ISSN 1045-9227. Lennart Ljung. System identification : theory for the user. Prentice Hall information and system sciences series. Prentice Hall PTR, Upper Saddle River, N.J., 2nd edition, 1999. Lennart Ljung and Torsten Soderstrom. Theory and practice of recursive identification. The MIT Press series in signal processing, optimization, and control. MIT Press, Cambridge, Mass., 1983. David G. Luenberger and Yinyu Ye. Linear and nonlinear programming, volume 116 of International series in operations research & management science. Springer, New York, 3 edition, 2008. ISBN 0387745025, 9780387745022, 0387745033, 9780387745039. Jan Marian Maciejowski. Predictive control : with constraints. Prentice Hall, Harlow, England, 2002. M. Y. Mashor. Hybrid multilayered perceptron networks. International Journal of Systems Science, 31(6):771–785, 2000. M. Y. Mashor. Performance comparison between hmlp, mlp and rbf networks with application to on-line system identification. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on, volume 1, pages 643–648, 2004. M.Y. Mashor. Modified recursive prediction error algorithm for training layered neural network. International Journal of the Computer, the Internet and Management, 11 (2):24–36, 2003. R. May, G. Dandy, and H. Maier. Review of Input Variable Selection Methods for Artificial Neural Networks, chapter 2, pages 19–44. InTech, 2011. D.Q. Mayne, J.B. Rawlings, C.V. Rao, and P.O.M. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, 36(6):789 – 814, 2000. ISSN 0005-1098. doi: 10.1016/S0005-1098(99)00214-9. URL http://www.sciencedirect. com/science/article/pii/S0005109899002149.
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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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Chapter 4 NEURAL NETWORK BASED SYST
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4.2 THE ARTIFICIAL NEURAL NETWORKS
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4.3 SYSTEM IDENTIFICATION WITH NEUR
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4.4 SUMMARY 117 Segment 1 Segment 2
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Chapter 5 NN BASED SYSTEM IDENTIFIC
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5.2 OFF-LINE BASED SYSTEM IDENTIFIC
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.6 ON-LINE SYSTEM IDENTIFICATION 1
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5.7 MODEL PERFORMANCE COMPARISON US
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5.8 SUMMARY 151 methods such as wei
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5.8 SUMMARY 153 would enable the im
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156 CHAPTER 6 NEURAL NETWORK BASED
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Page 225 and 226: 7.3 EXPERIMENTAL RESULTS 193 Flight
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Page 229 and 230: 7.3 EXPERIMENTAL RESULTS 197 indica
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Page 233 and 234: 7.3 EXPERIMENTAL RESULTS 201 Yaw Ra
Page 235 and 236: 7.3 EXPERIMENTAL RESULTS 203 mainta
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Page 246 and 247: 214 REFERENCES B. W. Bequette. Non-
Page 248 and 249: 216 REFERENCES Konstantinos Dalamag
Page 252 and 253: 220 REFERENCES B. Mettler, Mark B.
Page 254 and 255: 222 REFERENCES V. R. Puttige and S.
Page 256 and 257: 224 REFERENCES D.H. Shim. Hierarchi
Page 258 and 259: 226 REFERENCES Nikos Vitzilaios and
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