Reciprocity and Duality in a Fuzzy Network Model

P. Saha and A. Konar

References

  1. [1] A. Konar, Artificial intelligence and soft computing: Behavioraland cognitive modeling of the human brain (Boca Raton, FL: CRC Press, 1999).
  2. [2] P. Jackson, Introduction to expert systems (Reading, MA: Addison-Wesley, 1988).
  3. [3] L.A. Zadeh, The role of fuzzy logic in the management ofuncertainty in expert system, Fuzzy Sets and Systems, 11,1983, 199–227. doi:10.1016/S0165-0114(83)80081-5
  4. [4] D.A. Waterman & F. Hayes-Roth, Pattern directed inferencesystems (New York: Academic Press, 1977).
  5. [5] A.J. Bugarin & S. Barro, Fuzzy reasoning supported by Petrinets, IEEE Trans. on Fuzzy Systems, 2(2), 1994, 135–150. doi:10.1109/91.277962
  6. [6] T. Cao & A.C. Sanderson, A fuzzy Petri net approach toreasoning about uncertainty in robotic systems, Proc. IEEEInt. Conf. Robotics and Automation, Atlanta, GA, May 1993,317–322. doi:10.1109/ROBOT.1993.292001
  7. [7] T. Cao, Variable reasoning and analysis about uncertaintywith fuzzy Petri nets, in M.A. Marson (Ed.), Lecture Notes inComputer Science, 691 (New York: Springer-Verlag, 1993).
  8. [8] T. Cao & A.C. Sanderson, Task sequence planing using fuzzyPetri nets, IEEE Trans. on Systems, Man and Cybernetics,25(5), 1995, 755–769. doi:10.1109/21.376489
  9. [9] J. Cardoso, R. Valette, & D. Dubois, Petri nets with uncertainmarkings, in G. Rozenberg (Ed.), Advances in Petri Nets,Lecture Notes in Computer Science, 483 (New York: Springer-Verlag, 1990).
  10. [10] S.M. Chen, J.S. Ke, & J.F. Chang, Knowledge representationusing fuzzy Petri nets, IEEE Trans. on Knowledge and DataEngineering, 2(3), 1990, 311–319. doi:10.1109/69.60794
  11. [11] S.M. Chen, A new approach to inexact reasoning for rule-basedsystems, Cybernetic Systems, 23, 1992, 561–582. doi:10.1080/01969729208927482
  12. [12] A. Daltrini, Modeling and knowledge processing based onthe extended fuzzy Petri nets, master’s thesis, UNICAMP-FEEODCA, May 1993.
  13. [13] M.L. Garg, S.I. Ashon, & P.V. Gupta, A fuzzy Petri net forknowledge representation and reasoning, Information Processing Letters, 39, 1991, 165–171. doi:10.1016/0020-0190(91)90114-W
  14. [14] A. Konar & A.K. Mandal, Uncertainty management in expertsystems using fuzzy Petri nets, IEEE Trans. on Knowledgeand Data Engineering, 8(1), 1996, 96–105. doi:10.1109/69.485639
  15. [15] A. Konar & B. Biswas, Reasoning in a fuzzy decision supportsystem for criminal investigation, in G. Tonfoni & L. Jain(Eds.), Innovations in decision support systems (Adelaide:Advanced Knowledge International, 2003).
  16. [16] A. Konar & A.K. Mandal, Non-monotonic reasoning in expert systems using fuzzy Petri nets, Advances in Modeling & Analysis, B, 23 (1), 1992, 51–63.
  17. [17] A. Konar, Uncertainty management in expert system usingfuzzy Petri nets, doctoral diss., Jadavpur University, India,1994.
  18. [18] A. Konar & S. Pal, Modeling cognition with fuzzy neural nets,in C.T. Leondes (Ed.), Fuzzy theory systems: Techniques andapplications (New York: Academic Press, 1999).
  19. [19] H.P. Lipp & G. Gunther, A fuzzy Petri net concept forcomplex decision making process in production control, Proc.1st European Congress on Fuzzy and Intelligent Technology(EUFIT ’93), 1, Aachen, Germany, 1993, 290–294.
  20. [20] C.G. Looney, Fuzzy Petri nets for rule-based decision making,IEEE Trans. on Systems, Man and Cybernetics, 18 (1), 1988,178–183. doi:10.1109/21.87067
  21. [21] W. Pedrycz & F. Gomide, A generalized fuzzy Petri net model,IEEE Trans. on Fuzzy Systems, 2 (4), 1994, 295–301. doi:10.1109/91.324809
  22. [22] W. Pedrycz, Fuzzy sets engineering (Boca Raton, FL: CRCPress, 1995).
  23. [23] H. Scarpelli & F. Gomide, High level fuzzy Petri nets andbackward reasoning, in B. Bouchon-Meunier, R.R. Yager, & L.A. Zadeh (Eds.), Fuzzy logic and soft computing (Singapore: World Scientific, 1995).
  24. [24] J. Sil & A. Konar, A hybrid approach to knowledge acquisitionusing neural Petri nets and DS Theory, International Journalof Computational Intelligence and Applications, 1 (2), 2001,203–223. doi:10.1142/S1469026801000196
  25. [25] H. Scarpelli, F. Gomide, & R. Yager, A reasoning algorithmfor high level fuzzy Petri nets, IEEE Trans. on Fuzzy Systems,4(3), 1996, 282–295. doi:10.1109/91.531771
  26. [26] S.K. Yu, Comments on “knowledge representation using fuzzyPetri nets, IEEE Trans. on Knowledge and Data Engineering,7 (1), 1995, 190–191.
  27. [27] S.K. Yu, Knowledge representation and reasoning using fuzzyPr\T net-systems, Fuzzy Sets and Systems, 75, 1995, 33–45. doi:10.1016/0165-0114(94)00326-3
  28. [28] K. Hirota & W. Pedrycz, OR-AND neuron in modeling fuzzyset connectives, IEEE Trans. on Fuzzy systems, 2 (2), 1994,151–161. doi:10.1109/91.277963
  29. [29] T. Murata, Petri nets: Properties, analysis and applications,Proc. IEEE, 77 (4), 1989, 541–580. doi:10.1109/5.24143
  30. [30] J. Pearl, Distributed revision of composite beliefs, ArtificialIntelligence, 33, 1987, 173–215. doi:10.1016/0004-3702(87)90034-8
  31. [31] G. Shafer, A mathematical theory of evidence (Princeton, NJ:Princeton University Press, 1976).
  32. [32] S.A. Hutchinson & A.C. Kak, Planning sensing strategies ina robot workcell with multisensor capabilities, IEEE Trans.on Robotics and Automation, 5 (6), 1989, 765–783. doi:10.1109/70.88098
  33. [33] S. Pal & A. Konar, Cognitive reasoning using fuzzy neuralnets, IEEE Trans. on Systems, Man and Cybernetics, 26 (4),1996, 616–619. doi:10.1109/3477.517036
  34. [34] W. Pedrycz & F. Gomide, An introduction to fuzzy sets:Analysis and design (Cambridge, MA: MIT Press, 1998).
  35. [35] P. Saha, Abductive reasoning with inverse fuzzy discreterelations, doctoral diss., Jadavpur University, India, 2001.
  36. [36] P. Saha & A. Konar, A heuristic algorithm for computingthe Max-Min inverse fuzzy relation, International Journal ofApproximate Reasoning, 30, 2002, 131–147. doi:10.1016/S0888-613X(02)00069-5
  37. [37] I. Graham & P.L. Jones, Expert systems: Knowledge, uncertainty and decision (London: Chapman & Hall, 1988).
  38. [38] B. Kosko, Neural networks and fuzzy systems (EnglewoodCliffs, NJ: Prentice-Hall, 1994).
  39. [39] V. McDermott & J. Doyle, Non-monotonic logic I, ArtificialIntelligence, 13 (1–2), 1980, 41–72. doi:10.1016/0004-3702(80)90012-0
  40. [40] J. Doyle, Truth maintenance systems, Artificial Intelligence,12 (3), 1979, 231–272. doi:10.1016/0004-3702(79)90008-0
  41. [41] B. Biswas, Fuzzy decision support systems for criminal investigation, doctoral diss., Jadavpur University, India, 2002.

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