Municipal Creditworthiness Modelling by Radial Basis Function Neural Networks and Sensitive Analysis of Their Input Parameters

1. Loviscek, L.A., Crowley, F.D.: Municipal Bond Ratings and Municipal Debt Management. Marcel Dekker, New York (2003)

   Google Scholar 

2. Olej, V., Hajek, P.: Hierarchical Structure of Fuzzy Inference Systems Design for Municipal Creditworthiness Modelling. WSEAS Transactions on Systems and Control 2, 162–169 (2007)

   Google Scholar 

3. Olej, V., Hajek, P.: Modelling of Municipal Rating by Unsupervised Methods. WSEAS Transactions on Systems 7, 1679–1686 (2006)

   Google Scholar 

4. Hájek, P., Olej, V.: Municipal creditworthiness modelling by kohonen’s self-organizing feature maps and LVQ neural networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 52–61. Springer, Heidelberg (2008)

   Chapter  Google Scholar 

5. Hajek, P., Olej, V.: Municipal creditworthiness modelling by kohonen’s self-organizing feature maps and fuzzy logic neural networks. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 533–542. Springer, Heidelberg (2008)

   Chapter  Google Scholar 

6. Broomhead, D.S., Lowe, D.: Multivariate Functional Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)

   MathSciNet  MATH  Google Scholar 

7. Moody, J., Darken, C.J.: Fast Learning in Networks of Locally Tuned Processing Units. Neural Computing 1, 281–294 (1989)

   Article  Google Scholar 

8. Poggio, T., Girosi, F.: Regularization Algorithms for Learning that are Equivalent to Multilayer Networks. Science 247, 978–982 (1990)

   Article  MathSciNet  MATH  Google Scholar 

9. Niyogi, P., Girosi, F.: On the Relationship between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions. Massachusetts Institute of Technology Artificial Intelligence Laboratory, Massachusetts (1994)

   Google Scholar 

10. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall Inc., Englewood Cliffs (1999)

    MATH  Google Scholar 

11. Poggio, T., Girosi, F.: Networks for Approximation and Learning. Proc. of the IEEE 78, 1481–1497 (1990)

    Article  MATH  Google Scholar 

12. Park, J., Sandberg, I.W.: Universal Approximation using Radial Basis Function Network. Neural Computing 3, 246–257 (1991)

    Article  Google Scholar 

13. Wettschereck, D., Dietterich, T.: Improving the Performance of Radial Basis Function Networks by Learning Center Locations. In: Advances in Neural Information Processing Systems, vol. 4, pp. 1133–1140. Morgan Kaufman Publishers, San Francisco (1992)

    Google Scholar 

14. Orr, M.J.: Regularisation in the Selection of Radial Basis Function Center. Neural Computing 7, 606–623 (1995)

    Article  Google Scholar 

15. Tou, J.T., Gonzales, R.C.: Pattern Recognition Principles. Addition-Walley Publishing Comp., Massachusetts (1974)

    Google Scholar 

16. Kohonen, T.: Self-organizing Maps. Springer, New York (2001)

    Book  MATH  Google Scholar 

17. Carpenter, G.A., Grossberg, S., Reynolds, J.H.: ARTMAP: Supervised Real-time Learning and Classification of Non-stationary Data by a Self-organizing Neural Network. Neural Networks 5, 565–588 (1991)

    Article  Google Scholar 

18. Speckt, D.F.: Probabilistic Neural Networks. Neural Networks 1, 109–118 (1990)

    Article  Google Scholar 

19. Cristianini, N., Shawe-Taylor, J.: Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)

    Book  MATH  Google Scholar 

20. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Cambridge (2006)

    MATH  Google Scholar 

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