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1996-1997 | 24 | 2 | 169-188
Tytuł artykułu

Recursive self-tuning control of finite Markov chains

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A recursive self-tuning control scheme for finite Markov chains is proposed wherein the unknown parameter is estimated by a stochastic approximation scheme for maximizing the log-likelihood function and the control is obtained via a relative value iteration algorithm. The analysis uses the asymptotic o.d.e.s associated with these.
Rocznik
Tom
24
Numer
2
Strony
169-188
Opis fizyczny
Daty
wydano
1997
otrzymano
1995-10-04
poprawiono
1996-04-02
Twórcy
  • Department of Computer Science and Automation, Indian Institute of Science, Bangalore-560012, India
Bibliografia
  • [1] D. Bertsekas, Dynamic Programming--Deterministic and Stochastic Models, Prentice-Hall, Englewood Cliffs, N.J., 1987.
  • [2] V. S. Borkar, Identification and adaptive control of Markov chains, Ph.D. Thesis, Dept. of Electrical Engrg. and Computer Science, Univ. of California, Berkeley, 1980.
  • [3] V. S. Borkar, Topics in Controlled Markov Chains, Pitman Res. Notes in Math. 240, Longman Scientific and Technical, Harlow, 1991.
  • [4] V. S. Borkar, The Kumar-Becker-Lin scheme revisited, J. Optim. Theory Appl. 66 (1990), 289-309.
  • [5] V. S. Borkar, On Milito-Cruz adaptive control scheme for Markov chains, ibid. 77 (1993), 385-393.
  • [6] V. S. Borkar and K. Soumyanath, A new analog parallel scheme for fixed point computation I--theory, submitted.
  • [7] V. S. Borkar and P. P. Varaiya, Adaptive control of Markov chains I: finite parameter case, IEEE Trans. Automat. Control AC-24 (1979), 953-957.
  • [8] V. S. Borkar and P. P. Varaiya, Identification and adaptive control of Markov chains, SIAM J. Control Optim. 20 (1982), 470-488.
  • [9] Y.-S. Chow and H. Teicher, Probability Theory: Independence, Interchangeability, Martingales, Springer, New York, 1979.
  • [10] B. Doshi and S. Shreve, Randomized self-tuning control of Markov chains, J. Appl. Probab. 17 (1980), 726-734.
  • [11] Y. El Fattah, Recursive algorithms for adaptive control of finite Markov chains, IEEE Trans. Systems Man Cybernet. SMC-11 (1981), 135-144.
  • [12] --, Gradient approach for recursive estimation and control in finite Markov chains, Adv. Appl. Probab. 13 (1981), 778-803.
  • [13] M. Hirsch, Convergent activation dynamics in continuous time networks, Neural Networks 2 (1987), 331-349.
  • [14] A. Jalali and M. Ferguson, Adaptive control of Markov chains with local updates, Systems Control Lett. 14 (1990), 209-218.
  • [15] P. R. Kumar and A. Becker, A new family of adaptive optimal controllers for Markov chains, IEEE Trans. Automat. Control AC-27 (1982), 137-142.
  • [16] P. R. Kumar and W. Lin, Optimal adaptive controllers for Markov chains, ibid., 756-774.
  • [17] H. Kushner and D. Clark, Stochastic Approximation for Constrained and Unconstrained Systems, Springer, Berlin, 1978.
  • [18] P. Mandl, Estimation and control in Markov chains, Adv. Appl. Probab. 6 (1974), 40-60.
  • [19] R. Milito and J. B. Cruz Jr., An optimization oriented approach to adaptive control of Markov chains, IEEE Trans. Automat. Control AC-32 (1987), 754-762.
  • [20] J. Neveu, Discrete-Parameter Martingales, North-Holland, Amsterdam, 1975.
  • [21] B. Sagalovsky, Adaptive control and parameter estimation in Markov chains: a linear case, IEEE Trans. Automat. Control AC-27 (1982), 414-417.
  • [22] Ł. Stettner, On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model, Appl. Math. Optim. 27 (1993), 161-177.
  • [23] T. Yoshizawa, Stability Theory by Liapunov's Second Method, The Mathematical Society of Japan, 1966.
Typ dokumentu
Bibliografia
Identyfikatory
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bwmeta1.element.bwnjournal-article-zmv24i2p169bwm
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