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128    3. NEURAL NETWORK BLACK BOX APPROACH TO THE MODELING AND CONTROL OF DYNAMICAL SYSTEMS

                         [52] Guh RS, Shiue YR. Fast and accurate recognition of con-  [71] Elanayar S, Shin YC. Radial basis function neural net-
                             trol chart patterns using a time delay neural network. J  work for approximation and estimation of nonlinear
                             Chin Inst Ind Eng 2010;27(1):61–79.          stochastic dynamic systems. IEEE Trans Neural Netw
                         [53] Yazdizadeh A, Khorasani K, Patel RV. Identification of a  1994;5(4):594–603.
                             two-link flexible manipulator using adaptive time delay  [72] Pal C, Kayaba N, Morishita S, Hagiwara I. Dynamic sys-
                             neural networks. IEEE Trans Syst Man Cybern, Part B,  tem identification by neural network: A new fast learn-
                             Cybern 2010;30(1):165–72.                    ing method based on error back propagation. JSME Int J
                         [54] Juang JG, Chang HH, Chang WB. Intelligent automatic  Ser C, Dyn Control Robot Des Manuf 1995;38(4):686–92.
                             landing system using time delay neural network con-  [73] Ilyin VE. Attack aircraft and fighter-bombers. Moscow:
                             troller. Appl Artif Intell 2003;17(7):563–81.  Victoria-AST; 1998 (in Russian).
                         [55] Sun Y, Babovic V, Chan ES. Multi-step-ahead model er-  [74] Astolfi A. Nonlinear and adaptive control: Tools and al-
                             ror prediction using time-delay neural networks com-  gorithms for the user. London: Imperial College Press;
                             bined with chaos theory. J Hydrol 2010;395:109–16.  2006.
                         [56] Zhang J, Wang Z, Ding D, Liu X. H ∞ state estima-  [75] Astolfi A, Karagiannis D, Ortega R. Nonlinear and
                             tion for discrete-time delayed neural networks with ran-  adaptive control with applications. Berlin: Springer;
                             domly occurring quantizations and missing measure-  2008.
                             ments. Neurocomputing 2015;148:388–96.   [76] Gros C. Complex and adaptive dynamical systems: A
                         [57] Yazdizadeh A, Khorasani K. Adaptive time delay neural  primer. Berlin: Springer; 2008.
                             network structures for nonlinear system identification.
                                                                      [77] Ioannou P, Fidan B. Adaptive control tutorial. Philadel-
                             Neurocomputing 2002;77:207–40.               phia, PA: SIAM; 2006.
                         [58] Ren XM, Rad AB. Identification of nonlinear systems  [78] Ioannou P, Sun J. Robust adaptive control. Englewood
                             with unknown time delay based on time-delay neural  Cliffs, NJ: Prentice Hall; 1995.
                             networks. IEEE Trans Neural Netw 2007;18(5):1536–41.
                                                                      [79] Ioannou P, Sun J. Optimal, predictive, and adaptive con-
                         [59] Ljung L, Glad T. Modeling of dynamic systems. Engle-
                                                                          trol. Englewood Cliffs, NJ: Prentice Hall; 1994.
                             wood Cliffs, NJ: Prentice Hall; 1994.
                         [60] Arnold VI. Mathematical methods of classical mechan-  [80] Sastry S, Bodson M. Adaptive control: Stability, conver-
                                                                          gence, and robustness. Englewood Cliffs, NJ: Prentice
                             ics. 2nd ed.. Graduate texts in mathematics, vol. 60.  Hall; 1989.
                             Berlin: Springer; 1989.
                         [61] Krasovsky AA, editor. Handbook of automatic control  [81] Spooner JT, Maggiore M, Ordóñez R, Passino KM. Sta-
                                                                          ble adaptive control and estimation for nonlinear sys-
                             theory. Moscow: Nauka; 1987 (in Russian).
                         [62] Brumbaugh, R.W. An aircraft model for the AIAA con-  tems: Neural and fuzzy approximator techniques. New
                             trol design challenge, AIAA Guidance, Navigation and  York, NY: John Wiley & Sons, Inc.; 2002.
                                                                      [82] Tao G. Adaptive control design and analysis. New York,
                             Control Conf., New Orleans, LA, 1991. AIAA Paper–
                                                                          NY: John Wiley & Sons, Inc.; 2003.
                             91–2631, 12.
                         [63] Etkin B, Reid LD. Dynamics of flight: Stability and con-  [83] Omatu S, Khalid M, Yusof R. Neuro-control and its ap-
                             trol. 3rd ed.. New York, NY: John Wiley & Sons, Inc.;  plications. London: Springer; 1996.
                             2003.                                    [84] Leondes CT. Control and dynamic systems: Neural net-
                         [64] Boiffier JL. The dynamics of flight: The equations.  work systems techniques and applications. San Diego,
                             Chichester, England: John Wiley & Sons; 1998.  London: Academic Press; 1998.
                         [65] Cook MV. Flight dynamics principles. Amsterdam: El-  [85] Omidvar O, Elliott DL. Neural systems for control. San
                             sevier; 2007.                                Diego, London: Academic Press; 1997.
                         [66] Hull DG. Fundamentals of airplane flight mechanics.  [86] Nguyen HT, Prasad NR, Walker CL, Walker EA. A first
                             Berlin: Springer; 2007.                      course in fuzzy and neural control. London, New York:
                         [67] Gill PE, Murray W, Wright MH. Practical optimization.  Chapman & Hall/CRC; 1997.
                             London, New York: Academic Press; 1981.  [87] Letov AM. Flight dynamics and control. Moscow:
                         [68] Varela L, Acuña S. Handbook of optimization theory:  Nauka Publishers; 1969 (in Russian).
                             Decision analysis and applications. New York: Nova  [88] Piyavsky SA, Brusov VS, Khvilon EA. Optimiza-
                             Science Publishers, Inc.; 2011.              tion of parameters for multipurpose aircraft. Moscow:
                         [69] Ljung L. System identification: Theory for the user. 2nd  Mashinostroyeniye Publishers; 1974 (in Russian).
                             ed.. Upper Saddle River, NJ: Prentice Hall; 1999.  [89] DiGirolamo R. Flight control law synthesis using neu-
                         [70] Conti M, Turchetti C. Approximation of dynamical sys-  ral network theory. In: AIAA Guid., Navig. and Control
                             tems by continuous-time recurrent approximate iden-  Conf., Hilton Head Island, S.C., Aug. 10–12, 1992: Col-
                             tity neural networks. Neural Parallel Sci Comput  lect. Techn. Pap. Pt. 1. AIAA–92–4390–CP. Washington
                             1994;2(3):299–320.                           (D.C.); 1992. p. 385–94.
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