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Neural Network Approach to Design of Distributed Hard Real-Time Systems

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Computational Intelligence (Fuzzy Days 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1625))

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Abstract

This paper examines the utility of neural networks for optimization problems occuring in the design of distributed hard real-time systems. In other words, it describes how neural networks may also be used to solve some combinatorial optimization problems, such as: computer locations in distributed system, minimalization of overall costs, maximization of system reliability and availability, etc. All requested parameters and constraints in this optimization process fullfil the conditions for design of distributed hard real-time systems. We show that the neural network approach is useful to obtain the good results in the optimization process. Numerical experimentation confirms the appropriateness of this approach.

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References

  1. Aarts, E., van Laarhoven, P.: Simulated Annealing: Theory and Practice, John Wiley and Sons, New York, 1989

    Google Scholar 

  2. Betro, B. Schoen, F.: Sequential Stopping Rules for the Multistart Algorithm in Global Optimization, Mathematical Programming, Vol. 38 (1987) 271–286

    Article  MATH  MathSciNet  Google Scholar 

  3. Betro, B., Schoen, F.: Optimal and Sub-Optimal Stopping Rules for the Multistart Algorithm in Global Optimization, Mathematical Programming, Vol. 57 (1992) 445–458

    Article  MATH  MathSciNet  Google Scholar 

  4. Boender, C.G.E., Rinnooy Kan, A.H.G.: On When to Stop Sampling for the Maximum, Journal of Global Optimization, 1 (1991) 331–340

    Article  MATH  MathSciNet  Google Scholar 

  5. Burke, L.I.: Neural Methods for the Traveling Salesman Problem: Insights From Operations Research, Neural Networks, Vol. 7, No. 4 (1994) 681–690

    Article  MATH  Google Scholar 

  6. Burns, A.: Distributed Hard Real-Time Systems: What Restrictions Are Necessary, in: H.S.M. Zedan (Ed.): Real-Time Systems, Theory and Applications, North-Holland, Amsterdam, 1990, 297–303

    Google Scholar 

  7. Cichocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing, John Wiley, New York, 1993

    MATH  Google Scholar 

  8. Feller, W.: An Introduction in Probability Theory and Its Applications, Vol. II, John Wiley, New York, 1966

    Google Scholar 

  9. Haykin, S.: Neural Networks: A Comprehensive Foundation, IEEE Computer Society Press and Macmillan, New York, 1994

    MATH  Google Scholar 

  10. Hertz, J., Kroch, A., Palmer, R.G.: Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, 1991

    Google Scholar 

  11. Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. Natl. Acad. Sci. USA, Vol. 79 (1982) 2554–2558

    Article  MathSciNet  Google Scholar 

  12. Hopfield, J.J., Tank, D.W.: Neural Computation of Decisions in Optimization Problems, Biological Cybernetics, Vol. 52 (1985) 141–152

    MATH  MathSciNet  Google Scholar 

  13. Horst, R., Pardalos, P.M.: Handbook of Global Optimization, Kluwer Academic Publ., Dordrecht, 1995

    MATH  Google Scholar 

  14. Karhunen, J., Joutsensalo, J.: Generalization of Principal Component Analysis, Optimization Problems, and Neural Networks, Neural Networks, Vol. 8, No. 4, (1995) 549–567

    Article  Google Scholar 

  15. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by Simulated Annealing, Science, Vol. 220 (1983) 670–680

    Article  MathSciNet  Google Scholar 

  16. Korbicz, J., Obuchowicz, A., Uciński, D.: Artificial Neural Networks, Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1994 (in Polish)

    MATH  Google Scholar 

  17. Krishna, C.M., Shin, K.G.: Performance Measures for Multiprocessor Controllers, in: A. K. Agrawala, S. K. Tripathi (Eds.), Performance’83, North-Holland Publ. Comp., Amsterdam, 1983, 229–250

    Google Scholar 

  18. Kurose, J.F., Simha, K.: A Microeconomic Approach to Optimal Resource Allocation in Distributed Computer Systems, IEEE Trans. on Computers, Vol. 38, No. 5 (1989) 705–717

    Article  Google Scholar 

  19. Martyna, J.: Reliability Analysis of Distributed Systems for the Hard Real-Time Environment, Archiwum Informatyki Teoretycznej i Stosowanej, Vol. 6, No. 1–4 (1994) 89–100

    MATH  Google Scholar 

  20. Martyna, J.: Functional Availability Analysis of Hard Real-Time Distributed Systems, Archiwum Informatyki Teoretycznej i Stosowanej, Vol 6, No. 1–4 (1994) 101–114

    Google Scholar 

  21. Martyna, J.: A Methodology for the Design of Hard Real-Time Distributed Systems, Archiwum Informatyki Teoretycznej i Stosowanej, Vol 7, No. 1–4 (1995) 105–124

    Google Scholar 

  22. Martyna, J.: Application of Genetic Algorithms to Computer Assignment Problem in Distributed Hard Real-Time Systems, Prace Naukowe UJ, Zeszyty Informatyczne, Vol. 8 (1998) 45–62; and also in: B. Reusch (Ed.): Computational Intelligence. Theory and Applications, Lecture Notes in Computer Science, Vol. 1226, Springer-Verlag, Berlin, 1997, 564

    Google Scholar 

  23. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H.: Teller, E.: Equations of State Calculations by Fast Computing Machines, Journal of Chemical Physics, Vol. 21 (1953)

    Google Scholar 

  24. Mitra, D., Romeo, F., Sangiovani-Vincentelli, A.: Convergence and Finite-time Behavior of Simulated Annealing, Advances in Applied Probability, Vol. 18, No. 3 (1986)

    Google Scholar 

  25. Shang, Y., Wah, B.W.: Global Optimization for Neural Network Training, IEEE Computer, No. 3 (1996) 45–54

    Article  Google Scholar 

  26. Stankovic, J.A.: Misconceptions About Real-Time Computing. A Serious Problem for Next-Generation Systems, IEEE Computer, Vol. 21, No. 10 (1988) 10–19

    Google Scholar 

  27. Tang, Z., Koehler, G.J.: Deterministic Global Optimal FNN Training Algorithms, Neural Networks, Vol. 7, No. 2 (1994) 301–311

    Article  Google Scholar 

  28. Tank, D.W., Hopfield, J.J.: Simple “Neural” Optimization Networks: An A/D Converter, Signal Decision Circuit, and Linear Programming Circuit, IEEE Trans. on Circuits and Systems, Vol. CAS-33, No. 5 (1986) 533–541

    Article  Google Scholar 

  29. Xu, X., Tsai, W.T.: Effective Neural Algorithms for the Traveling Salesman Problem, Neural Networks, Vol. 4 (1991) 193–205

    Article  Google Scholar 

  30. Zhao, D.N., Cherkassky, V., Baldwin, T.R., Olson, D.E.: A Neural Network Approach to Job-Shop Scheduling, IEEE Trans. on Neural Networks, Vol. 2, No. 1 (1991) 175–179

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Jagiellonian University, ul. Nawojki 11, 30-072, Kraków, Poland

    Jerzy Martyna

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  1. Jerzy Martyna
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Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, D-44221, Dortmund, Germany

    Bernd Reusch

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© 1999 Springer-Verlag Berlin Heidelberg

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Martyna, J. (1999). Neural Network Approach to Design of Distributed Hard Real-Time Systems. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_15

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  • DOI: https://doi.org/10.1007/3-540-48774-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66050-7

  • Online ISBN: 978-3-540-48774-6

  • eBook Packages: Springer Book Archive

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