REFERENCES Basheer, I.A. and Hajmeer, M. (2000), “Artificial neural networks: fundamentals, computing, design, and application”, Journal of Microbiological Methods, Elsevier Science, 43: 3-31. Becker, G.S. (1976), The Economic Approach to Human Behavior, the University of Chicago Press, 1976. Collan, M., Eklund, T., and Back, B. (2007), “Using the Self-Organizing Map to Visualize and Explore Socio-Economic Development”, EBS Review, 22(1): 6-15. Costea A., and Năstac, I. (2005), “Assessing the Predictive Performance of ANN Classifiers Based on Different Data Preprocessing Methods”, Internat. Journal of Intelligent Sys. Acc. Fin. Mgmt. vol. 13, issue 4 (December 2005), pp. 217-250, DOI: 10.1002/isaf. 269, John Wiley & Sons, Wiley InterScience. Demuth, H. and Beale, M. (2001), Neural Network Toolbox, The MathWorks, Inc., Natick. Dobrescu, E. (2006), Macromodels of the Romanian Market Economy, Editura Economică, Bucharest. Eklund, T., B. Back, H. Vanharanta, A. Visa (2003), “Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking”, Information Visualization, 2(3): 171-181. Hagan, M.T., Demuth, H.B., and Beale, M. (1996), Neural Networks Design, MA: PWS Publishing, Boston. Hornik, K., Stinchcombe, M. and White H. (1989), “Multilayer feedforward networks are universal approximators”, Neural Networks, 2: 359-366. Jackson, J.E. (1991), A user guide to principal components, John Wiley, New York. Moller, M.F. (1993), “A scaled conjugate gradient algorithm for fast supervised learning”, Neural Networks, 6: 525-533. Năstac, I., Dobrescu, E. and Pelinescu, E. (2007), “Neuro-Adaptive Model for Financial Forecasting”, Romanian Journal of Economic Forecasting, Vol. 4, 8(3): 19-41. Năstac, I., (2004), “An Adaptive Retraining Technique to Predict the Critical Process Variables”, TUCS Technical Report, No. 616, June 2004, Turku, Finland (http://www.tucs.fi/research/series/serie.php?type=techreport&year =2004). Năstac, I., and Costea, A. (2004), “A Retraining Neural Network Technique for Glass Manufacturing Data Forecasting”, Proceedings of IJCNN 2004, Vol. 4, IEEE Print, Budapest, pp. 2753-2758. Năstac, I., and Cristea, P. (2005), “Neuro-Adaptive Forecasting for Nonstationary Sequences”, Proceedings of IEEE-SOFA 2005, pp. 179-186. Năstac, I., and Matei, R. (2003), “Fast retraining of artificial neural networks”, in Guoyin Wang et al. (Eds.), Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Springer-Verlag, in the series of Lecture Notes in Artificial Intelligence (LNAI 2639), pp. 458-462. O’Leary, D.E. (1998), “Using Neural Networks to Predict Corporate Failure”, International Journal of Intelligent Systems in Accounting, Finance & Management 7, pp. 187-197. Pendharkar, P.C. (2002), “A computational study on the performance of artificial neural networks under changing structural design and data distribution”, European Journal of Operational Research 138 (2002) 155-177. Sanfey, A.G., Rilling, J.K., Aronson, J.A., Nystrom, L.E., Cohen, J.D. (2003), “The Neural Basis of Economic Decision-Making in the Ultimatum Game”, Science, June 2003, Vol. 300. No. 5626, pp. 1755 – 1758. Shiller R.J. (1999), “Human behavior and the efficiency of the financial system”, Chapter 20 in Handbook of Macroeconomics, Elsevier 1999, vol. 1, Part C, pp 1305-1340. Zupan, B., Demšar, J., Kattan, M.W., Ohori, M., Graefen, M., Bohanec, M., Beck, J.R. (2001), Orange and Decisions-At-Hand: “Bridging predictive data mining and decision support”, IDDM-2001: ECML/PKDD-2001 Workshop Integrating Aspects of Data Mining, Decision Support and Meta-Learning (eds. Giraud-Carrier, C., Lavrač, N., Moyle, S., Kavšek, B.), Freiburg, pp. 151-162.