Adaptation of Cases for Case Based Forecasting with Neural Network Support

  • Juan M. Corchado
  • B. Lees

Abstract

A novel approach to the combination of a case based reasoning system and an artificial neural network is presented in which the neural network is integrated within the case based reasoning cycle so that its generalizing ability may be harnessed to provide improved case adaptation performance. The ensuing hybrid system has been applied to the task of oceanographic forecasting in a real-time environment and has produced very promising results. After presenting classifications of hybrid artificial intelligence problem-solving methods, the particular combination of case based reasoning and neural networks, as a problem-solving strategy, is discussed in greater depth. The hybrid artificial intelligence forecasting model is then explained and the experimental results obtained from trials at sea are outlined.

Keywords

Steam Propa Beach Lime Hunt 

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References

  1. 1.
    Lees B., Rees, N. and Aiken, J. (1992). Knowledge-based océanographic data analysis, Proc. Expersys-92, Attia F., Flory A., Hashemi S., Gouarderes G. and Marciano J. (eds), IITT International Paris, October 1992, pp. 561–565.Google Scholar
  2. 2.
    Medsker L. R. (1995). Hybrid Intelligent Systems. Kluwer Academic Publishers, Boston, MA.MATHCrossRefGoogle Scholar
  3. 3.
    Sun R. (1996). Commonsense reasoning with rules, cases, and connectionist models: a paradigmatic comparison. Fuzzy Sets and Systems, 82(2), 187–200.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Lees B. (1999). Hybrid case based reasoning: an overview. Int. Workshop on Hybrid CBR Systems, ICCBR 99, Munich, July, 1999.Google Scholar
  5. 5.
    Bezdek J. C. (1994). What is Computational Intelligence?, in Computational Intelligence: Imitating Life, Zurada J. M., Marks II R. J. and Robinson C. J. (eds). IEEE Press, New York, pp. 1–12.Google Scholar
  6. 6.
    Soucek B. and IRIS group (1991). Neural and Intelligent Systems Integration. Wiley, New York.Google Scholar
  7. 7.
    Medsker L. R. and Bailey D. L. (1992). Models and guidelines for integrating expert systems and neural networks, in Hybrid Architectures for Intelligent Systems, Kandel A. and Langholz G. (eds). CRC Press, Boca Raton, FL, pp 131–164.Google Scholar
  8. 8.
    López de Mántaras R. and Plaza E. (1997). Case-based reasoning: an overview. AI Communications, 10, 21–29.Google Scholar
  9. 9.
    Hunt J. and Miles R. (1994). Hybrid case based reasoning. Knowledge Engineering Review, 9(4), 383–397.CrossRefGoogle Scholar
  10. 10.
    Corchado J. M., Lees B., Fyfe C., Rees N. and Aiken J. (1998). Neuro-adaptation method for a case based reasoning system. International Joint Conference on Neural Networks. Anchorage, AK, May 4–9, pp. 713–718.Google Scholar
  11. 11.
    Sun R. and Alexandre F. (1997). Connnectionist-Symbolic Integration: From Unified to Hybrid Approaches. Lawrence Erlbaum Associates, Mahwah, NJ.Google Scholar
  12. 12.
    Reategui E. B., Campbell J. A. and Leao B. F. (1996). Combining a neural network with case based reasoning in a diagnostic system. Artificial Intelligence in Medicine, 9(1), 5–27.CrossRefGoogle Scholar
  13. 13.
    Bezdek J. C. and Jazayeri K. (1989). A connectionist approach to case based reasoning, in K. J. Hammond (ed.), Proceedings of the Case Based Reasoning Workshop, Pensacola Beach, FL, Morgan Kaufmann, San Mateo, CA, pp. 213–217.Google Scholar
  14. 14.
    Thrift P. (1989). A neural network model for case based reasoning, in Hammond K. J., (ed.), Proceedings of the Case Based Reasoning Workshop, Pensacola Beach, FL. Morgan Kaufmann, San Mateo, CA, pp. 334–337.Google Scholar
  15. 15.
    Alpaydin G. (1991). Networks that grow when they learn and shrink when they forget. Technical Report TR 91-032. International Computer Science Institute, May 1991.Google Scholar
  16. 16.
    Lim H. C., Lui A., Tan H. and Teh H. H. (1991). A connectionist case based diagnostic expert system that learns incrementally. Proceedings of the International Joint Conference on Neural Networks, pp. 1693–1698.Google Scholar
  17. 17.
    Azcarraga A. and Giacometti A. (1991). A prototype-based incremental network model for classification tasks. Fourth International Conference on Neural Networks and their Applications, Nimes, France, pp. 78–86.Google Scholar
  18. 18.
    Malek M. (1995). A connectionist indexing approach for CBR systems, in Veloso M. and Aamodt A., (eds), Case-Based Reasoning: Research and Development. First International Conference, ICCBR-95. Sesimbra, Portugal. Springer Verlag, London, pp. 520–527.Google Scholar
  19. 19.
    Quan Mao, Jing Qin, Xinfang Zhang and Ji Zhou. (1994). Case prototype based design: philosophy and implementation, in Ishii K. (ed.), Proc. Computers in Engineering Vol.1, 11–14 September, Minneapolis, MN. ASME, New York, pp. 369–374.Google Scholar
  20. 20.
    Main J., Dillon T. S. and Khosla R. (1996). Use of fuzzy feature vectors and neural networks for case retrieval in case based systems. Proc. Biennial Conference of the North American Fuzzy Information Processing Society — NAFIPS. IEEE, Piscataway, NJ, pp. 438–443.Google Scholar
  21. 21.
    Richter A. M. and Weiss S. (1991). Similarity, uncertainty and case based reasoning in PATDEX, in Boyer R. S. (ed.), Automated Reasoning. Kluwer, Boston, MA, pp. 249–265.CrossRefGoogle Scholar
  22. 22.
    Garcia Lorenzo M. M. and Bello Pérez R. E. (1996). Model and its different applications to case based reasoning. Knowledge-Based Systems. 9(7), 465–473.CrossRefGoogle Scholar
  23. 23.
    Agre G. and Koprinska, I. (1996). Case based refinement of knowledge-based neural networks. In Albus J., Meystel A. and Quintero R. (eds), Proceedings of the International Conference on Intelligent Systems: A Semiotic Perspective, Vol.II, pp. 37–45.Google Scholar
  24. 24.
    Reategui E. B. and Campbell J. A. (1995). A classification system for credit card transactions, in Haton J. P., Keane M. and Manago M. (eds), Advances in Case Based Reasoning: Second European Workshop. EWCBR-94, Chantilly, France. Springer-Verlag, London, pp. 280–291.Google Scholar
  25. 25.
    Liu Z. Q. and Yan F. (1997). Fuzzy neural network in case based diagnostic system IEEE Transactions on Fuzzy Systems, 5(2), 209–222.CrossRefGoogle Scholar
  26. 26.
    Aamodt A. and Langseth H. (1998). Integrating Bayesian networks into knowledge-intensive CBR. AAAI’98, Workshop Technical Report WS-98-15, Case Base Reasoning Integrations, 27 July 1998, Wisconsin, pp. 1–6.Google Scholar
  27. 27.
    Dingsoyr T. (1998). Retrieval of cases by using a Bayesian network. AAAI’98, Workshop Technical Report WS-98-15, Case Base Reasoning Integrations, 27 July 1998, Wisconsin, pp. 50–54.Google Scholar
  28. 28.
    Shinmori A. (1998). A proposal to combine probabilistic reasoning with case-based retrieval for software troubleshooting. AAAI’98, Workshop Technical Report WS-98-15, Case Base Reasoning Integrations, 27 July 1998, Wisconsin, pp. 149–154.Google Scholar
  29. 29.
    Friese T. (1999). Utilization of Bayesian belief networks for explanation-driven case based reasoning. IJCAI’ 99. Workshop ML-5: Automating the Construction of Case Based Reasoners, Stockholm, Sweden, pp. 73–76.Google Scholar
  30. 30.
    Mao Q., Qin J., Zhang X. and Zhou J. (1994). Case prototype based design: philosophy and implementation, in Ishii K. (ed.), Proc. Computers in Engineering, Vol. 1, 11–14 September, Minneapolis, MN, pp. 369–374.Google Scholar
  31. 31.
    Palmen E. and Newton C. W. (1969). Atmospheric Circulations Systems. Academic Press, London, p. 602.Google Scholar
  32. 32.
    Tomczak M. and Godfrey J. S. (1994). Regional Oceanography: An Introduction. Pergamon, New York.Google Scholar
  33. 33.
    Corchado J. M., Lees, B., Fyfe, C. and Rees, N. (1997). Adaptive agents: learning from the past and forecasting the future, Proc. PADD97-First International Conference on the Practical Application of Knowledge Discovery and Data Mining, London, 23–25 April, Practical Application Co., pp. 109–123.Google Scholar
  34. 34.
    Aamodt A. and Plaza E. (1994). Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.Google Scholar
  35. 35.
    Watson I. and Marir F. (1994). Case-Based Reasoning: A Review. The Knowledge Engineering Review, Vol. 9, No. 3. Cambridge University Press, Cambridge, UK.Google Scholar
  36. 36.
    Rees N., Aiken J. and Corchado J. M. (1997). Internal Report: STEB Implementation. PML, Plymouth, UK, 30 September.Google Scholar
  37. 37.
    Wess S., Althoff K-D. and Derwand, G. (1994). Using K-D trees to improve the retrieval step in case-based reasoning, in Wess S., Althoff K-D. and Richter M. M. (eds), Topics in Case Based Reasoning, Springer-Verlag, Berlin, pp. 167–181.Google Scholar
  38. 38.
    Corchado J. M., Rees N. and Aiken J. (1996). Internal Report on Supervised ANN s and Oceanographic Forecasting. PML, Plymouth, UK, 30 December.Google Scholar
  39. 39.
    Aha D. W. (1990). A study of instance-based learning algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations. Technical Report 90-42. University of California, Department of Information and Computer Science, Irvine, CA.Google Scholar
  40. 40.
    Corchado J. M. (2000) Neuro-symbolic model for real-time forecasting problems. Ph.D. dissertation, University of Paisley, UK.Google Scholar
  41. 41.
    Fritzke B. (1994). Fast learning with incremental RBF networks. Neural Processing Letters. 1(1), 2–5.CrossRefGoogle Scholar
  42. 42.
    Bishop C. R. (1995). Neural Networks for Pattern Recognition. Clarendon Press, Oxford.Google Scholar

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© Springer-Verlag London Limited 2001

Authors and Affiliations

  • Juan M. Corchado
  • B. Lees

There are no affiliations available

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