Hybrid Intelligent Decision Support Systems and Applications for Risk Analysis and Discovery of Evolving Economic Clusters in Europe

  • N. Kasabov
  • L. Erzegovesi
  • M. Fedrizzi
  • A. Beber
  • D. Deng
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 45)


Decision making in a complex, dynamically changing environment is a difficult task that requires new techniques of computational intelligence for building adaptive, hybrid intelligent decision support systems (HIDSS). Here, a new approach is proposed based on evolving agents in a dynamic environment. Neural network and rule-based agents are evolved from incoming data and expert knowledge if a decision making process requires this. The agents are evolved from methods included in a repository for intelligent connectionist based information systems RICBIS (http://divcom. otago. ac. nz/infosci/kel/CBIIS. html) with the use of financial market data collected in an on-line mode, and with the use of macroeconomic data published monthly in the European Central Bank Bulletin. RICBIS includes different types of neural networks, including MLP, SOM, fuzzy neural networks (FuNN), evolving fuZzy neural networks (EFuNN), evolving SOM, rule-based systems, data pre-processing techniques, standard statistical and financial techniques. A case study project on risk analysis of the European Monetary Union (EMU) is considered and a framework of a system EMU-HIDSS is presented, which deals with different levels of information and users, e.g. the whole world, Europe, clusters of nations, a single nation, companies/banks. It combines modules for final decision making, global and national economic development, exchange rate trend prediction, stock index trend prediction, etc. Some experimental results on real data are presented.


Intelligent decision support systems risk analysis connectionist-based information systems. 


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  1. 1.
    Amari, S., Kasabov, N., Eds. (1998) Brain-like Computing and Intelligent Information Systems. Springer Verlag.Google Scholar
  2. 2.
    Bollacker, K., Lawrence S., Giles L. (1998) CiteSeer: An autonomous Web agent for automatic retrieval and identification of interesting publications. Proc. of the 2nd International ACM conference on autonomous agents. ACM Press, 116–123.Google Scholar
  3. 3.
    Deboeck, G. (1999) Investment maps for emerging markets. In: N. Kasabov and R. Kozma Eds. Neuro-fuzzy techniques for intelligent information systems. Physica Verlag (Springer), 373–395.Google Scholar
  4. 4.
    Deng, D., Kasabov, N. (1999) Evolving Self-organizing Map and its Application in Generating A World Macroeconomic Map. Proc. of ICONIP/ANZIIS/ANNES’99 International Workshop, University of Otago, 7–12.Google Scholar
  5. 5.
    Deboeck, G., Kohonen, T. (1998) Visual exploration in finance with self-organizing maps, Springer Verlag.CrossRefGoogle Scholar
  6. 6.
    Eichengreen, B., Rose, A., Wyplosz, C. (1995) Exchange market mayhem: the antecedents and aftermath of speculative attacks. Economic Policy, 251–312.Google Scholar
  7. 7.
    Monthly Bulletin (1999) European Central Bank.Google Scholar
  8. 8.
    Evolver software package for Excel,
  9. 9.
    Farmer, J.D., Sidorowitch J. (1987) Predicting chaotic time series. Phys. Rev. Lett., 59, 845–848.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Garcia, R., Gencay R. (1997) Pricing and hedging derivative securities with neural networks and a homogeneity hint. Technical Report, Department of Science and Economics, University of Montreal, Canada.Google Scholar
  11. 11.
    Goodman, R., Higgins, C.M., et. al. (1992) Rule-based neural networks for classification and probability estimation. Neural Computation, 14, 781–804.CrossRefGoogle Scholar
  12. 12.
    Goonatilake, S., Khebbal S. Eds. (1995). Intelligent Hybrid Systems. John Wiley & Sons London.Google Scholar
  13. 13.
    Goonatilake, S., Trelevan P. (1995) Intelligent Systems for Finance and Business. John Wiley & Sons.Google Scholar
  14. 14.
    Hashiyama, T., Furuhashi, T., Uchikawa, Y. (1992) A decision making model using a fuzzy neural network. Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, 1057–1060.Google Scholar
  15. 15.
    Heskes, T.M., Kappen B. (1993) On-line learning processes in artificial neural networks. Mathematical foundations of neural networks, Elsevier, Amsterdam, 199–233.Google Scholar
  16. 16.
    Hutchinson, J., Lo A., Poggio T. (1994) A nonparametric approach to pricing and hedging derivative securities via learning networks. The Journal of Finance, vol.XLIL, No.3, 851–890.CrossRefGoogle Scholar
  17. 17.
    Ishikawa, M. (1996) Structural learning with forgetting. Neural Networks, 9, 501–521.CrossRefGoogle Scholar
  18. 18.
    Kasabov, N. (1996) Adaptable connectionist production systems. Neurocomputing, 13(2–4) 95–117.CrossRefGoogle Scholar
  19. 19.
    Kasabov, N. (1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. The MIT Press, CA, MA.zbMATHGoogle Scholar
  20. 20.
    Kasabov, N. (1996) Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems, 82(2), 2–20.MathSciNetGoogle Scholar
  21. 21.
    Kasabov, N. (1998) The ECOS Framework and the ECO Learning Method for Evolving Connectionist Systems. Journal of Advanced Computational Intelligence, 2(6), 1–8.Google Scholar
  22. 22.
    Kasabov, N. (1998) ECOS: A framework for evolving connectionist systems and the ECO learning paradigm. Proc. of ICONIP’98, Kitakyushu, Japan, IOS Press, 1222–1235.Google Scholar
  23. 23.
    Kasabov, N. (1998) Evolving fuzzy neural networks — algorithms, applications and biological motivation. In Yamakawa and Matsumoto Eds., Methodologies for the Conception, Design and Application of Soft Computing. World Scientific. 271–274.Google Scholar
  24. 24.
    Kasabov, N. (1999) Evolving connectionist and fuzzy connectionist systems for on-line adaptive decision making and control. In: Advances in Soft Computing — Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry Eds. Springer, London.Google Scholar
  25. 25.
    Kasabov, N. (1999) Evolving fuzzy neural networks for adaptive, on-line intelligent agents and systems. In: O. Kaynak, S. Tosunoglu and M. Ang Eds. Recent Advances in Mechatronics. Springer, Berlin.Google Scholar
  26. 26.
    Kasabov, N. Fedrizzi, M. (1999) Fuzzy neural networks and evolving connectionist systems for intelligent decision making. Proc. of the 8th International Fuzzy Systems Association World Congress, Taiwan, 17–20.Google Scholar
  27. 27.
    Kasabov, N., Kozma, R. (1999) Multi-scale analysis of time series based on neuro-fuzzy chaos methodology applied to financial data. In Refenes, A., Burges, A. and Moody, B. Eds. Computational Finance 1997. Kluwer Academic.Google Scholar
  28. 28.
    Kasabov, N., Kim J.S. (1997) FuNN/2- a fuzzy neural network architecture for adaptive learning and knowledge acquisition. Information Sciences, 101(3–4), 155–175.CrossRefGoogle Scholar
  29. 29.
    Kasabov, N., Kozma, R. (1997) Neuro-fuzzy-chaos engineering for building intelligent adaptive information systems. In: Intelligent Systems: Fuzzy Logic, Neural Networks and Genetic Algorithms. Da Ruan Eds. Kluwer Academic Boston/London/Dordrecht, 213–237.Google Scholar
  30. 30.
    Kasabov, N., Israel, S., Woodford, B. (1999) Methodology and evolving connectionist ar chitecture for image pattern recognition. In: Pal, Ghosh and Kundu Eds. Soft Computing and Image Processing, Physica-Verlag(Springer) Heidelberg, accepted.Google Scholar
  31. 31.
    Kasabov, N., Song, Q. (1999) Dynamic, evolving fuzzy neural networks with ‘m-out-of-n activation nodes for on-line adaptive systems. Technical Report TR99–04, Department of Information Science, University of Otago.Google Scholar
  32. 32.
    Kasabov, N., Watts M. (1997) Genetic algorithms for structural optimisation, dynamic adaptation and automated design of fuzzy neural networks. In: Proc. of the Inter. Conf. on Neural Networks (ICNN97), IEEE Press, Houston.Google Scholar
  33. 33.
    Kasabov, N., Watts, M. (1999) Spatial-temporal evolving fuzzy neural networks STEFuNNs and applications for adaptive phoneme recognition, Technical Report TR99–03, Department of Information Science, University of Otago.Google Scholar
  34. 34.
    Kasabov, N., Woodford, B. (1999) Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems. Proc. of Int. Conf. FUZZ-IEEE, Seoul.Google Scholar
  35. 35.
    Kawahara, S., Saito, T. (1996) On a novel adaptive self-organising network. Cellular Neural Networks and Their Applications, 41–46.Google Scholar
  36. 36.
    Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, 78, 1464–1497.Google Scholar
  37. 37.
    Kohonen, T. (1997) Self-Organizing Maps, second edition. Springer.zbMATHCrossRefGoogle Scholar
  38. 38.
    Kozma, R., Kasabov, N. (1998) Chaos and fractal analysis of irregular time series embedded into connectionist structure. In: Brain-like Computing and Intelligent Information Systems. S. Amari, N. Kasabov Eds. Springer Singapore, 213–237.Google Scholar
  39. 39.
    Kozma, R., Kasabov, N. (1999) Generic neuro-fuzzy-chaos methodologies and techniques for intelligent time-series analysis. In: Soft Computing in Financial Engineering, R. Ribeiro, R. Yager, H.J. Zimmermann, J. Kacprzyk (Eds.), Physica-Verlag Heidelberg.Google Scholar
  40. 40.
    Krogh, A., Hertz, J.A. (1992) A simple weight decay can improve generalisation. Advances in Neural Information Processing Systems 4, 951–957.Google Scholar
  41. 41.
    Le Cun, Y., Denker J.S., Solla, S.A. (1990) Optimal Brain Damage. In Touretzky, D.S. Ed. Advances in Neural Information Processing Systems, 2, 598–605.Google Scholar
  42. 42.
    Lin, C.T., Lee C.S.G. (1996) Neuro Fuzzy Systems. Prentice Hall.Google Scholar
  43. 43.
    Mitchell, M.T. (1997) Machine Learning, MacGraw-Hill.zbMATHGoogle Scholar
  44. 44.
    Moody, J., Darken, C. (1989) Fast learning in networks of locally-tuned processing units. Neural Computation, 1, 281–294.CrossRefGoogle Scholar
  45. 45.
    Persaud, A. (1998) Global foreign exchange research. In: Event Risk Indicator Handbook, JP Morgan, London, 44–171.Google Scholar
  46. 46.
    Saad, D. Ed. (1999) On-line Learning in Neural Networks. Cambridge University Press.Google Scholar
  47. 47.
    Sankar, A., Mammone, R.J. (1993) Growing and pruning neural tree networks. IEEE Trans. Comput., 42(3), 291–299.CrossRefGoogle Scholar
  48. 48.
    Swope, J.A., Kasabov, N., Williams, M. (1999) Neuro-fuzzy modelling of heart rate signals and applications to diagnostics. In: Szepanjuk Ed., Fuzzy Systems in Medicine, Physica Verlag, accepted.Google Scholar
  49. 49.
    Trippi, R., Turban E. Eds. (1993) Neural Networks in Finance and Investing. Irwin Professional Publications, New York.Google Scholar
  50. 50.
    Vaga, T. (1990) The coherent market hypothesis. Financial Analysts Journal, November December, 36–49.Google Scholar
  51. 51.
    Watts, M., Kasabov, N. (1998) Genetic algorithms for the design of fuzzy neural networks. In: Proc. of ICONIP’98, Kitakyushu, Japan, 21–23.Google Scholar
  52. 52.
    Watts, M., Kasabov, N. (1999) Neuro-genetic tools and techniques. In: Neuro-Fuzzy Techniques for Intelligent Information Systems, N. Kasabov and R. Kozma, Eds., Physica Verlag Heidelberg, 97–110.Google Scholar
  53. 53.
    Woldrige, M., Jennings, N. (1995) Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10.Google Scholar
  54. 54.
    Zirilli, J. (1997) Financial Prediction Using Neural Networks. Thomson Computer Press, London.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • N. Kasabov
    • 1
  • L. Erzegovesi
    • 2
  • M. Fedrizzi
    • 2
  • A. Beber
    • 2
  • D. Deng
    • 1
  1. 1.Dept. of Information ScienceUniv. of OtagoDunedinNew Zealand
  2. 2.Dept. of Informatics and Faculty of EconomicsUniv. of TrentoItaly

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