Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

REFERENCES

• K.Abrougui and M.Elhadef, Parallel self-diagnosis of large multiprocessor systems under the generalized comparison model, in Proceedings of the 11th International Conference on Parallel and Distributed Systems, Japan2005, pp. 78–84.
   Google Scholar
• L.C.P.Albini, E.P.A.Duarte, Jr., and R.P.Ziwich, A generalized model for distributed comparison-based system-level diagnosis, J. Braz. Comput. Soc.10 (2005), pp. 44–56.
   Google Scholar
• F.Allan, T.Kameda, and S.Toida, An approach to the diagnosability analysis of a system, IEEE Trans. Comput. (1975), pp. 1040–1042.
   Web of Science ®Google Scholar
• B.Ayeb, Fault Identification Algorithmic: A new formal approach, in Proceedings of the 29th International Symposium on Fault-Tolerant Computing, IEEE Computer Society Publications1999, pp. 138–145.
   Google Scholar
• M.Barborak, M.Malek, and A.Dahbura, The consensus problem in fault-tolerant computing, ACM Comput. Surv.25(2) (1993), pp. 171–220.
   Web of Science ®Google Scholar
• F.Barsi, F.Grandoni, and P.Maestrini, A theory of diagnosability without repairs, IEEE Trans. Comput.C-25 (1976), pp. 585–593.
   Web of Science ®Google Scholar
• D.M.Blough and H.W.Brown, The broadcast comparison model for on-line fault diagnosis in multiprocessor systems: Theory and implementation, IEEE Trans. Comput.48(5) (1999), pp. 470–493.
   Web of Science ®Google Scholar
• D.Blough and A.Pelc, Complexity of fault diagnosis in comparison models, IEEE Trans. Comput.41(3) (1992), pp. 318–324.
   Web of Science ®Google Scholar
• K.Chwa and S.Hakimi, On fault identification in diagnosable systems, IEEE Trans. Comput.C-30 (1981), pp. 414–422.
   Web of Science ®Google Scholar
• A.M.Corluhan and S.L.Hakimi, On an algorithm for identifying faults in a t-diagnosable system, in Proceedings of the Conference on Information Science Systems, Johns Hopkins University, Baltimore, MD1976, pp. 370–375.
   Google Scholar
• A.T.Dahbura and G.M.Masson, Self-implicating structures for diagnosable systems, in Proceedings of the 13th International Symposium on Fault-Tolerant Computing, IEEE Computer Society Publications1983, pp. 332–335.
   Google Scholar
• A.Dahbura and G.Masson, A practical variation of the O(n2.5) fault diagnosis algorithm, in Proceedings of the 14th International Symposium on Fault-Tolerant Computing, IEEE Computer Society Publications1984.
   Google Scholar
• A.Dahbura and G.Masson, An O(n2.5) fault identification algorithm for diagnosable systems, IEEE Trans. Comput.C-33(6) (1984)
   Google Scholar
• E.P.Duarte, Jr, R.P.Ziwich, and L.C.P.Albini, A survey of comparison-based system-level diagnosis, ACM Comput. Surv.43(3) (2011), pp. 22:1–22:56.
   Google Scholar
• M.Elhadef, Diagnosing permanent faults in distributed and parallel computing systems using artificial neural networks, in CDROM of the 24th International Parallel and Distributed Processing Symposium (IPDPS-2010) Workshops, ATLANTA, USA2010.
   Google Scholar
• M.Elhadef and B.Ayeb, An evolutionary algorithm for identifying faults in t-diagnosable systems, in Proceedings of the 19th IEEE Symposium on Reliable Distributed Systems (SRDS-2000), Nürnberg, Germany2000, pp. 74–83.
   Google Scholar
• M.Elhadef and B.Ayeb, Self-diagnosis of multiprocessor systems under generalized comparison model, in Proceedings of the 14th International Conference on Parallel and Distributed Computing Systems, Dallas, TX, USA2001, pp. 372–379.
   Google Scholar
• M.Elhadef and A.Nayak, Comparison-based system-level fault diagnosis: A neural network approach, IEEE Trans. Parallel Distrib. Syst.23(6) (2012), pp. 1047–1059.
   Web of Science ®Google Scholar
• M.Elhadef, S.Das, and A.Nayak, Genetic algorithm for identifying faults in large diagnosable systems, Int. J. Parallel Emergent Distrib. Syst.20(2) (2005), pp. 113–125.
   Google Scholar
• M.Elhadef, S.Das, and A.Nayak, System-level fault diagnosis using comparison models: An artificial-immune-systems-based approach, J. Netw.1(2) (2006), pp. 239–251.
   Google Scholar
• M.Elhadef, A.Nayak, and N.Zeng, Ants vs. faults: A swarm intelligence approach for diagnosing distributed computing networks, in Proceedings of the 13th International Conference on Parallel and Distributed Systems (ICPADS-2007), Vol. 1, Los Alamitos, CA, USA2007, pp. 1–8.
   Google Scholar
• S.L.Hakimi and A.T.Amin, Characterization of the connection assignment of diagnosable systems, IEEE Trans. Comput.C-23 (1974), pp. 86–88.
   Web of Science ®Google Scholar
• S.L.Hakimi and K.Y.Chwa, Schemes for fault tolerant computing: A comparison of modularly redundant and t-diagnosable systems, Inform. Contr.49 (1981), pp. 212–238.
   Google Scholar
• S.Hakimi, S.Maheshwari, and A.Amin, On a graph model of diagnosable systems, in Proceedings of the 4th Annual Pittsburgh Conference in Modeling and Simulation, Vol. 4, Pittsburgh, PA1973, pp. 220–223.
   Google Scholar
• Hopfield Network, http://en.wikipedia.org/wiki/Hopfield_network.
   Google Scholar
• J.J.Hopfield and D.W.Tank, “Neural” computation of decisions in optimization problems, Biol. Cybern.52 (1985), pp. 141–152.
   PubMed Web of Science ®Google Scholar
• T.Kameda, S.Toida, and F.Allan, A diagnosing algorithm for networks, Inform. Contr.29 (1975), pp. 141–148.
   Google Scholar
• M.A.Kennedy and G.G.L.Meyer, The PMC system level fault model: Cardinality properties of the implied faulty sets, IEEE Trans. Comput.38 (1989), pp. 478–480.
   Web of Science ®Google Scholar
• W.E.Kozlowski and H.Krawczyk, A comparison-based approach to multicomputer system diagnosis in hybrid fault situations, IEEE Trans. Comput.40(11) (1991), pp. 1283–1287.
   Web of Science ®Google Scholar
• E.Kranakis and A.Pelc, Better adaptive diagnosis of hypercubes, IEEE Trans. Comput.49(10) (2000), pp. 1013–1020.
   Web of Science ®Google Scholar
• S.Kreutzer and S.L.Hakimi, Adaptive fault identification in two new diagnostic models, in Proceedings of the 21st Allerton Conference on Communication, Control and Computing, University of Illinois, Urbana, IL1983, pp. 353–362.
   Google Scholar
• O.Lazaro and D.Girma, A Hopfield neural-network-based dynamic channel allocation with handoff channel reservation control, IEEE Trans. Vehicular Technol.49(5) (2000), pp. 1578–1587.
   Web of Science ®Google Scholar
• S.Lee and K.Shin, Probabilistic diagnosis of multiprocessor systems, Vol. 26, no. 1, ACM Comput. Surv., New York, USA, 1994, pp. 121–139.
   Web of Science ®Google Scholar
• R.Madden, On fault-set identification in some system level diagnostic models, in Proceedings of the International Symposium on Fault-Tolerant Computing, IEEE Computer Society Publications1977.
   Google Scholar
• J.Maeng and M.Malek, A comparison connection assignment for self-diagnosis of multiprocessor systems, in Proceedings of the 11th International Symposium on Fault-Tolerant Computing, 1981, pp. 173–175.
   Google Scholar
• M.Malek, A comparison connection assignment for diagnosis of multiprocessor systems, in Proceedings of the 7th International Symposium on Computer Architecture, New York, Association for Computing Machinery Publications1980, pp. 31–35.
   Google Scholar
• B.T.Nassu, E.P.Duarte, and A.T.R.Pozo, Comparison of evolutionary algorithms for system-level diagnosis, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), Washington, DC, USA2005.
   Google Scholar
• J.K.Paik and A.K.Katsaggelos, Image restoration using a modified Hopfield network, IEEE Trans. Image Process.1(1) (1992), pp. 49–63.
   PubMed Web of Science ®Google Scholar
• F.P.Preparata, G.Metze, and R.T.Chien, On the connection assignment problem of diagnosable systems, IEEE Trans. Electron. Comput.16(6) (1967), pp. 848–854.
   Web of Science ®Google Scholar
• V.B.Rao and H.Rao, C++, Neural Networks and Fuzzy Logic, 2nd ed., MIS Press, New York, 1995.
   Google Scholar
• A.Sengupta and A.Dahbura, On self-diagnosable multiprocessor systems: Diagnosis by the comparison approach, IEEE Trans. Comput.41(11) (1992), pp. 1386–1396.
   Web of Science ®Google Scholar
• J.Shawe-Taylor and N.Cristianini, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, New York, USA, 2000.
   Google Scholar
• G.Sullivan, An O(t3+|E|) fault identification algorithm for diagnosable systems, IEEE Trans. Comput.37(4) (1988), pp. 388–397.
   Web of Science ®Google Scholar
• H.Yang, M.Elhadef, A.Nayak, and X.Yang, Network fault diagnosis: An artificial immune system approach, in Proceedings of the 14th International Conference on Parallel and Distributed Systems (ICPADS-2007), Vol. 0, Los Alamitos, CA, USA2008, pp. 463–469.
   Google Scholar
• Y.Zhu and Z.Yan, Computerized tumor boundary detection using a Hopfield neural network, IEEE Trans. Med. Imaging16(1) (1997), pp. 55–67.
   PubMed Web of Science ®Google Scholar