Direct neural dynamic programming

Handbook of Learning and Approximate Dynamic Programming

Volumn , Issue , 2004, Pages 125-151

  Si, Jennie ^a   Yang, Lei ^a   Liu, Derong ^b  

^a Arizona State University   (United States)

^b 1120 Science and Engineering Offices   (United States)

Author keywords

Adaptation model; Approximation methods; Artificial neural networks; Dynamic programming; Learning; Learning systems; Markov processes

Indexed keywords

AIRCRAFT DETECTION; HEURISTIC ALGORITHMS; HEURISTIC PROGRAMMING; LEARNING SYSTEMS; MARKOV PROCESSES; NEURAL NETWORKS;

ACTION DEPENDENT HEURISTIC DYNAMIC PROGRAMMING; ADAPTATION MODELS; APPROXIMATION METHODS; COMPONENT FAILURES; DIRECT NEURAL DYNAMIC PROGRAMMING; HEURISTIC DYNAMIC PROGRAMMING; LARGE-SCALE PROBLEM; LEARNING;

DYNAMIC PROGRAMMING;

EID: 84893246005     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1109/9780470544785.ch5     Document Type: Chapter

References (28)

1. C. Anderson, Strategy learning with multi-layer connectionist representations, Proc. Fourth International Workshop on Machine Learning, pp. 103-114, 1987.
2. C. Anderson, Learning to control an inverted pendulum using neural networks, IEEE Control Systems Magazine, vol. 9, no. 3, pp. 31-37, 1989.
3. S. Ariyavisitakul, Signal and interference statistics of a CDMA system with feedback power control-Part II, IEEE Trans. Communications, vol. 42, pp. 597-605,1994.
4. A. G. Barto, R. S. Sutton, and C. W. Anderson, Neuron like adaptive elements that can solve difficult learning control problems, IEEE Trans. Systems, Man, and Cybernetics, vol. 13, pp. 834-847,1983.
5. D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont, MA, 1996.
6. Z. Dziong, M. Jia, and P. Mermelstein, Adaptive traffic admission for integrated services in CDMA wireless-access networks, IEEE Journal on Selected Areas in Communications, vol. 14, pp. 1737-1747,1996.
7. K. D. Eltohamy and C. Y. Kuo, Nonlinear optimal control of a triple link inverted pendulum with single control input, International Journal of Control, vol. 69, no. 2, pp. 239-256,1998.
8. R. A. Guerin, Channel occupancy time distribution in a cellular radio system, IEEE Trans. Vehicular Technology, vol. 35, pp. 89-99,1987.
9. D. Hong and S. S. Rappaport, Traffic model and performance analysis for cellular mobile radio telephone systems with prioritized and nonprioritized handoff procedures, IEEE Trans Vehicular Technology, vol. 35, pp. 77-92,1986.
10. D. K. Kim and D. K. Sung, Capacity estimation for an SIR-based powercontrolled CDMA system supporting ON-OFF traffic, IEEE Trans. Vehicular Technology, vol. 49, pp. 1094-1100, 2000.
11. D. Liu and Y. Zhang, An adaptive call admission control algorithm for SIR-based power-controlled integrated services CDMA cellular networks, Proc. International Conference on Telecommunications, pp. 610-638, 2002.
12. D. E. Moriarty and R. Miikkulainen, Efficient reinforcement learning through symbiotic evolution, Machine Learning, vol. 22, no. 11-32, pp. 11-32, 1996.
13. M. Pendrith, On reinforcement learning of control actions in noisy and nonMarkovian domains, Technical Report UNSW-CSE-TR-9410, 1994.
14. D. V. Prokhorov, R. A. Santiago, and D. C. Wunsch II, Adaptive critic designs: a case study for neuro-control, Neural Networks, vol. 8, pp. 1367-1372, 1995.
15. D. V. Prokhorov and D. C. Wunsch II, Adaptive critic designs, IEEE Trans. Neural Networks, vol. 8, no. 5, pp. 997-1007, 1997.
16. R. Ramjee, D. Towsley, and R. Nagarajan, On optimal call admission control in cellular networks, Wireless Networks, vol. 3, no. 1, pp. 29-41, 1997.
17. S. S. Rappaport and C. Purzynski, Prioritized resource assignment for mobile cellular communication systems with mixed services and platform types, IEEE Trans. Vehicular Technology, vol. 45, pp. 443-458, 1996.
18. S. M. Shin, C. H. Cho, and D. K. Sung, Interference-based channel assignment for DS-CDMA cellular systems, IEEE Trans. Vehicular Technology, vol. 48, pp. 233-239,1999.
19. J. Si and Y. Wang, Online learning control by association and reinforcement, IEEE Trans. Neural Networks, vol. 12, no. 2, pp. 264-276, 2001.
20. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
21. C. J. C. H. Watkins, Learning from Delayed Rewards, Ph.D. Thesis, University of Cambridge, UK, 1989.
22. P. Werbos, Advanced forecasting methods for global crisis warning and models of intelligence, General System Yearbook, vol. 22, pp. 25-38, 1977.
23. P. Werbos, A menu of design for reinforcement learning over time, Neural Networks for Control, in W. T. Miller III, R. S. Sutton, and P. J. Werbos (eds.), MIT Press, Cambridge, MA, 1990.
24. P. Werbos, Neuro-control and supervised learning: An overview and valuation, Handbook of Intelligent Control, in D. White and D. Sofge (eds.), Van Nostrand Reinhold, New York, 1992.
25. P. Werbos, Approximate dynamic programming for real-time control and neural modeling, Handbook of Intelligent Control, in D. White and D. Sofge (eds.), Van Nostrand Reinhold, New York, 1992.
26. P. Werbos, Tutorial on neurocontrol, control theory and related techniques: from back propagation to brain-like intelligent systems, 12th Inti Conference on Math. & Computer Modeling & Scientific Computing, 1999. Also available at http://www.iamcm.org/pwerbos/.
27. L. Yang, J. Si, K. Tsakalis, and A. Rodriguez, Analyzing and enhancing direct NDP designs using a control-theoretic approach, IEEE International Symposium on Intelligent Control, Houston, TX, pp. 529-532, 2003.
28. A guide to DECT features that influence the traffic capacity and the maintenance of high radio link transmission quality, including the results of simulations, ETSI Technical Report: ETR 042, 1992.