Skip to main content
SpringerLink
Log in

Unconstrained optimization problems

  • Chapter
  • First Online:
Learning Automata and Stochastic Optimization

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 225))

  • 128 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bi-Chong W, Luus R 1978 Reliability of optimization procedures for obtaining global optimum. AIChE Journal 24:619–626

    Google Scholar 

  2. Dorea C C Y 1990 Stopping rules for a random optimization method. SIAM J., Control and Optimization 28:841–850

    Google Scholar 

  3. Sorensen D C 1982 Newton's method with a model trust region modification. SIAM Journal Numer. Anal. 19:409–426

    Google Scholar 

  4. Dolan W B, Cummings P T, Le Van M D 1989 Process optimization via simulated annealing: application to network design. AIChE Journal 35:725–736

    Google Scholar 

  5. McMurtry G J, Fu K S 1966 A variable structure automaton used as a multimodal searching technique. IEEE Trans. on Automatic Control 11:379–387

    Google Scholar 

  6. Kurz W, Najim K 1992 Synthese neuronaler netze anhand strukturierter stochastischer automaten. Nachrichten Neuronale Netze Journal 2:2–6

    Google Scholar 

  7. Kushner H J 1972 Stochastic approximation algorithms for the local optimization of functions with nonunique stationary points. IEEE Trans. on Automatic Control 17:646–654

    Google Scholar 

  8. Poznyak A S, Najim K, Chtourou M 1993 Use of recursive stochastic algorithm for neural networks synthesis. Applied Mathematical Modelling 17:444–448

    Google Scholar 

  9. Najim K, Chtourou M 1994 Neural networks synthesis based on stochastic approximation algorithm. International Journal of Systems Science 25:1219–1222

    Google Scholar 

  10. Narendra K S, Thathachar M A L 1989 Learning Automata an Introduction. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  11. Baba N 1984 New Topics in Learning Automata Theory and Applications. Springer-Verlag, Berlin

    Google Scholar 

  12. Lakshmivarahan S 1981 Learning Algorithms Theory and Applications. Springer-Verlag, Berlin

    Google Scholar 

  13. Najim K, Oppenheim G 1991 Learning systems: theory and application. IEEE Proceedings∼E 138:183–192

    Google Scholar 

  14. Najim K, Poznyak A S 1994 Learning Automata: Theory and Applications. Pergamon Press, Oxford

    Google Scholar 

  15. Thathachar M A L, Harita B R 1987 Learning automata with changing number of actions. IEEE Trans. Syst. Man, and Cybern. 17:1095–1100

    Google Scholar 

  16. Najim K, Poznyak A S 1996 Multimodal searching technique based on learning automata with continuous input and changing number of actions. IEEE Trans. Syst. Man, and Cybern. 26:666–673

    Google Scholar 

  17. Poznyak A S, Najim K 1997 Learning automata with continuous input and changing number of actions. to appear in International Journal of Systems Science.

    Google Scholar 

  18. Bush R R, Mosteller F 1958 Stochastic Models for Learning. John Wiley & Sons, New York

    Google Scholar 

  19. Shapiro I J, Narendra K S 1969 Use of stochastic automata for parameter self optimization with multimodal performance criteria. IEEE Trans. Syst. Man, and Cybern. 5:352–361

    Google Scholar 

  20. Varshavskii V I, Vorontsova I P 1963 On the behavior of stochastic automata with variable structure. Automation and Remote Control 24:327–333

    Google Scholar 

  21. Ash B B 1972 Real Analysis and Probability. Academic Press, New York

    Google Scholar 

  22. Doob J L 1953 Stochastic Processes. John Wiley & Sons, New York

    Google Scholar 

  23. Robbins H, Siegmund D 1971 A convergence theorem for nonnegative almost supermartingales and some applications. In Rustagi J S (ed) 1971 Optimizing Methods in Statistics. Academic Press, New York

    Google Scholar 

  24. Poznyak A S, Najim K, Ikonen E 1996 Adaptive selection of the optimal order of linear regression models using learning automata. Int. J. of Systems Science 27:151–159

    Google Scholar 

  25. Najim K, Mészaros A, Rusnak A 1997 A stochastic optimization algorithm based on learning automata. Journal a

    Google Scholar 

  26. Najim K, Chtourou M, Thibault J 1992 Neural network synthesis using learning automata. Journal of Systems Engineering 2:192–197

    Google Scholar 

  27. Ikonen E, Najim K 1997 Use of learning automata in distributed fuzzy logic processor training. IEE Proceedings∼E

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag London Limited

About this chapter

Cite this chapter

(1997). Unconstrained optimization problems. In: Learning Automata and Stochastic Optimization. Lecture Notes in Control and Information Sciences, vol 225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0015106

Download citation

  • DOI: https://doi.org/10.1007/BFb0015106

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76154-9

  • Online ISBN: 978-3-540-40938-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation