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824   Neural Networks in Feedback Control Systems

                          80. J. A. Farrell, ‘‘Stability and Approximator Convergence in Nonparametric Nonlinear Adaptive Con-
                             trol,’’ IEEE Transactions on Neural Networks 9(5), 1008–1020 (1998).
                          81. J. Y. Choi and J. A. Farrell, ‘‘Nonlinear Adaptive Control Using Networks of Piecewise Linear
                             Approximators,’’ IEEE Transactions on Neural Networks 11(2), 390–401 (2000).
                          82. R. Zbikowski and K. J. Hunt, Neural Adaptive Control Technology, World Scientific, Singapore,
                             1996.
                          83. A. G. Barto, ‘‘Connectionist Learning for Control,’’ in Neural Networks for Control, W. T. Miller,
                             R. S. Sutton, P. J. Werbos (eds.), MIT Press, Cambridge, MA, 1991.
                          84. A. G. Barto and T. G. Dietterich, ‘‘Reinforcement Learning and Its Relationship to Supervised
                             Learning,’’ in Handbook of Learning and Approximate Dynamic Programming, J. Si, A. Barto, W.
                             Powell, and D. Wunsch (eds.), IEEE Press, West Conshohocken, PA, 2004.
                          85. R. Sutton, ‘‘Learning to Predict by the Method of Temporal Differences,’’ Machine Learning 3, 9–
                             44 (1988).
                          86. P. Dayan, ‘‘The Convergence of TD(
) for General 
,’’ Machine Learning 8(3–4), 341–362 (1992).
                          87. T. Landelius, ‘‘Reinforcement Learning and Distributed Local Model Synthesis,’’ Ph.D. Disserta-
                             tion, Linkoping University, 1997.
                                    ¨
                          88. S. T. Hagen and B. Krose, ‘‘Linear Quadratic Regulation Using Reinforcement Learning,’’ in Pro-
                             ceedings of the Eighth Belgian-Dutch Conference on Machine Learning, BENELEARN’98, F. Ver-
                             denius and W. van den Broek (eds.), Wageningen, October 1998, pp. 39–46.
                          89. D. V. Prokhorov amd L. A. Feldkamp, ‘‘Analyzing for Lyapunov Stability with Adaptive Critics,’’
                             in Proceedings of the International Conference on Systems, Man, Cybernetics, Dearborn, MI, 1998,
                             pp. 1658–1661.
                          90. X. Liu and S. N. Balakrishnan, ‘‘Convergence Analysis of Adaptive Critic Based Optimal Control,’’
                             in Proceedings of the American Control Conference, Chicago, IL, 2000, pp. 1929–1933.
                          91. C. Anderson, R. M. Kretchner, P. M. Young, and D. C. Hittle, ‘‘Robust Reinforcement Learning
                             Control with Static and Dynamic Stability,’’ International Journal of Robust and Nonlinear Control,
                             11 (2001).
                          92. D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, MA, 1996.
                          93. S. Ferrari and R. Stengel, ‘‘An Adaptive Critic Global Controller,’’ in Proceedings of the American
                             Control Conference, Anchorage, AK, 2002, pp. 2665–2670.
                          94. J. Murray, C. Cox, R. Saeks, and G. Lendaris, ‘‘Globally Convergent Approximate Dynamic Pro-
                             gramming Applied to an Autolander,’’ in Proc. ACC, Arlington, VA, 2001, pp. 2901–2906.
                          95. L. Feldkamp and D. Prokhorov, ‘‘Recurrent Neural Networks for State Estimation,’’ paper presented
                             at the Twelfth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, 2003, pp. 17–
                             22.
                          96. X. Liu and S. N. Balakrishnan, ‘‘Adaptive Critic Based Neuro-Observer,’’ in Proceedings of the
                             American Control Conference, Arlington, VA, 2001, pp. 1616–1621.
                          97. J. Si and Y.-T. Wang, ‘‘On-Line Control by Association and Reinforcement,’’ IEEE Transactions
                             on Neural Networks 12(2), 264–276 (2001).
                          98. D. Prokhorov and D. Wunsch, ‘‘Adaptive Critic Designs,’’ IEEE Transactions on Neural Networks
                             8(5) (1997).
                          99. J. N. Tsitsiklis, ‘‘Efficient Algorithms for Globally Optimal Trajectories,’’ IEEE Transactions on
                             Automatic Control 40(9), 1528–1538 (1995).
                          100. J. Campos and F. L. Lewis, ‘‘Adaptive Critic Neural Network for Feedforward Compensation,’’ in
                             Proceedings of the American Control Conference, San Diego, CA, June 1999.
                          101. G. A. Rovithakis, ‘‘Stable Adaptive Neuro-Control Via Lyapunov Function Derivative Estimation,’’
                             Automatica 37(8), 1213–1221 (2001).
                          102. J. Murray, C. Cox, G. Lendaris, and R. Saeks, ‘‘Adaptive Dynamic Programming,’’ IEEE Trans-
                             actions on Systems, Man, and Cybernetics 32(2) (2002).
                          103. L. Baird, ‘‘Reinforcement Learning in Continuous Time: Advantage Updating,’’ in Proceedings of
                             the International Conference on Neural Networks, Orlando, FL, June 1994.
                          104. K. Doya, ‘‘Reinforcement Learning in Continuous Time and Space,’’ Neural Computation 12, 219–
                             245 (2000).
                          105. S. E. Lyshevski, Control Systems Theory with Engineering Applications, Birkhauser, Berlin, 2001.
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