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A Neural Algorithm of Nonlinear Regression

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This article is devoted to the comparison between two alternative approaches to the economic models creation and to the estimation of their parameters. The first approach is to create a multiple regression model whereas the second one is to use neural modeling. Both methods are applied to estimation of an exemplary econometric model.
Rocznik
Tom
Strony
65--81
Opis fizyczny
Bibliogr. 21 poz., rys.
Twórcy
autor
  • Institute of Computer Science, Jagiellonian University, Nawojki 11, 30-072 Cracow, Poland
  • Institute of Computer Science, Jagiellonian University, Nawojki 11, 30-072 Cracow, Poland
autor
  • Department of Statistics and Computer Science, AWF, Kraków
Bibliografia
  • [1] Coats P.K., Fant F; Recognizing financial distress patterns using a neural net¬work tool, Financial Management, 22, 1993, pp. 142-156.
  • [2] Bielecki A.; Dynamical properties of learning process of weakly linear and nonlinear neurons (Simulation of a nonlinear econometric model using neuran networks), Nonlinear Analysis: Real World Applications, 2, 2001, pp. 249-258.
  • [3]Bielecki A., Bielecka M., Podolak I.T.; Symulacja pewnego nieliniowego modelu ekonometrycznego przy pomocy sieci neuronowych, Przegląd Statystyczny, 48,1- 2, 2001 (in Polish), pp. 151-159.
  • [4]Cherkassky V., Lari-Najafi H.; Constraint topological mapping for nonparametric regression analysis, Neural Networks, 4,1, 1991, pp. 27-40.
  • [5]Cybenko G.; Approximation by superposition of a sigmoidal function, Mathematics of Control, Signals, and Systems, 2, 1989, pp. 303-314.
  • [G] Hecht-Nielsen R.; Kolmogorov’s mapping neural network existence theorem, Proc. of Int. Conf. on Neural Networks, part. III, IEEE Press, New York, 1987.
  • [7]Hornik K.; Approximation capabilities of multilayer feed-forward networks, Neural Networks, 4,2, 1991, pp. 251-258.
  • [8]Hertz J., Krogh A., Palmer R.G.; Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, Massachusetts, 1991.
  • [9]Jóźwiak J., Podgórski J.; Statystyka od podstaw, (Foundations of Statistics), PWE, Warszawa 1997 (in Polish).
  • [10]Kryzanowski L., Galler M., Wright D.; Using artificial neural networks to pick stocks. Financial Analysts Journal, 49, 1993, pp. 21-27.
  • [11]Klapper R.; SpiderWeb http://www.cs.nyu.edu/~klap7794/spiderweb2.html
  • [12]Kolmogorov A.N.; On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, Doki. Akad. Nauk ZSRR. 114, 1957, pp. 953-956.
  • [13]Kondo T.; Short-term prediction of air pollution concentration by a neural network, Transactions of the Society of Instrument and Control Engineers, 3, 1993, pp. 710-718.
  • [14]Kurkova V.; Kolmogorov’s theorem and multilayer neural networks, Neural Networks, 5,3, 1992, pp. 501-506.
  • [15]Lula P.; Próba zastosowania algorytmów genetycznych do szacowania parametrów funkcji regresji (A genetic approach to estimate regression function parameters), Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 474, 1996 (in Polish), pp. 63-73.
  • [16]Müller B., Reinhardt J.; Neural Networks, Springer Verlag, New York 1990.
  • [17]Nowak E.; Propozycje wyznaczania parametrów pewnego nieliniowego modelu ekonometrycznego (Proposals for a nonlinear econometric model parameters estimation), Przegląd Statystyczny, 36, 1989 (in Polish), pp. 31-39.
  • [18]Podolak I.T.; Feedforward neural network’s sensitivity to input data representation, Computer Physics Communications, 117, 1999, pp. 181-188.
  • [19]Trippi R.R., DeSieno D.; Trading equity index futures with a neural network, Journal of Portfolio Management, Fall 1992, 1992, pp. 27-33.
  • [20]Venugopal V., Beats W.; Neural networks and statistical techniques in marketing research: a conceptual comparison, Marketing Intelligence k Planning, 12,7, 1994, pp. 30-38.
  • [21]Żurada J.; Introduction to Artificial Systems, West, Publishing Co., St.. Paul, USA, 1992.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ1-0019-0102
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