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2017, vol. 45, br. 1, str. 45-60
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Prediktivni hibridni sistem za berzansko tržište - slučaj tranzitornih tržišta
Hybrid system prediction for the stock market: The case of transitional markets
aUniverzitet u Novom Sadu, Fakultet tehničkih nauka bDžavni univerzitet u Novom Pazaru, Departman za matematičke nauke cUniverzitet Educons, Fakultet poslovne ekonomije, Sremska Kamenica
e-adresa: v_djakovic@uns.ac.rs
Projekat: Razvoj novih informaciono-komunikacionih tehnologija, korišćenjem naprednih matematičkih metoda, sa primenama u medicini, telekomunikacijama, energetici, zaštititi nacionalne baštine i obrazovanju (MPNTR - 44006)
Unapređenje konkurentnosti Srbije u procesu pristupanja Evropskoj uniji (MPNTR - 47028)
Sažetak
Predmet istraživanja u radu jeste kreiranje i testiranje poboljšanog fuzzy neural network backpropagation modela za predikciju berzanskih indeksa, uz poređenje sa tradicionalnim neural network backpropagation modelom. Cilj istraživanja jeste dolaženje do konkretnih saznanja o mogućnostima primene poboljšanog fuzzy neural network backpropagation modela za predikciju berzanskih indeksa, sa posebnim fokusom na tranzitorna tržišta. Metodologija korišćena u radu obuhvata integraciju fuzzy-fikovanih tezina u neuro mreži. Rezultati istraživanja biće korisni kako široj investicionoj javnosti, tako i akademskoj struci, u smislu korišćenja poboljšanog modela u donošenju odluka o investiranju i unapređenju znanja u predmetnoj oblasti.
Abstract
The subject of this paper is the creation and testing of an enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes, including the comparison with the traditional neural network backpropagation model. The objective of the research is to gather information concerning the possibilities of using the enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes focusing on transitional markets. The methodology used involves the integration of fuzzified weights into the neural network. The research results will be beneficial both for the broader investment community and the academia, in terms of the application of the enhanced model in the investment decision-making, as well as in improving the knowledge in this subject matter.
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Reference
|
|
  
|
Barbounis, T.G., Theocharis, J.B. (2007) Locally recurrent neural networks for wind speed prediction using spatial correlation. Information Sciences, 177(24): 5775-5797
|
|
|
Beale, E.M.L. (1972) A derivation of conjugate gradients. u: Lootsma F.A. [ur.] Numerical Methods for Nonlinear Optimization, London: Academic Press, 39-43
|
|
|
Casasent, D., Natarajan, S. (1995) A classifier neural net with complex-valued weights and square-law nonlinearities. Neural Networks, 8(6): 989-998
|
|
|
Cazorla, M.A., Escolano, F. (2003) Two bayesian methods for junction classification. IEEE Transactions on Image Processing, 12(3): 317-327
|
|
|
Cheng, L., Liu, J. (2014) An Optimized Neural Network Classifier for Automatic Modulation Recognition. TELKOMNIKA Indonesian Journal of Electrical Engineering, 12(2):
|
|
|
Draghici, S. (2002) On the capabilities of neural networks using limited precision weights. Neural Networks, 15(3): 395-414
|
|
|
Feuring, T. (1996) Learning in fuzzy neural networks. u: Proceedings of International Conference on Neural Networks (ICNN'96), Institute of Electrical and Electronics Engineers (IEEE), str. 1061-1066
|
|
1
|
Fletcher, R. (1964) Function minimization by conjugate gradients. Computer Journal, 7(2): 149-154
|
|
|
Gedeon, T. (1999) Additive neural networks and periodic patterns. Neural Networks, 12(4-5): 617-626
|
|
|
Hagan, M.T., Demuth, H.B., Beale, M.H. (1996) Neural network design. Boston: PWS Publishing
|
|
|
Ishibuchi, H., Tanaka, H., Okada, H. (1993) Fuzzy neural networks with fuzzy weights and fuzzy biases. u: IEEE International Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), str. 1650-1655
|
|
|
Ishibuchi, H., Morioka, K., Tanaka, H. (1994) A fuzzy neural network with trapezoid fuzzy weights. u: Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, Institute of Electrical and Electronics Engineers (IEEE), str. 228-233
|
|
|
Islam, M., Murase, K. (2001) A new algorithm to design compact two-hidden-layer artificial neural networks. Neural Networks, 14(9): 1265-1278
|
|
|
Kamarthi, S.V., Pittner, S. (1999) Accelerating neural network training using weight extrapolations. Neural Networks, 12(9): 1285-1299
|
|
2
|
Lin, C.T., Lee, C.S.G. (1996) Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Upper Saddle River: Prentice-Hall
|
|
|
Melin, P., Gonzalez, C., Bravo, D., Gonzalez, F., Martinez, G. (2006) Modular Neural Networks and Fuzzy Sugeno Integral for Face and Fingerprint Recognition. u: Abraham, Ajith; de Baets, Bernard; Köppen, Mario; Nickolay, Bertram [ur.] Applied Soft Computing Technologies: The Challenge of Complexity, Berlin-Heidelberg: Springer Nature, str. 603-618
|
|
|
Meltser, M., Shoham, M., Manevitz, L.M. (1996) Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm. Neural Networks, 9(6): 965-978
|
|
1
|
Moller, A.F. (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw, 6: 525-533
|
|
|
Neville, R.S., Eldridge, S. (2002) Transformations of sigma-pi nets: obtaining reflected functions by reflecting weight matrices. Neural Networks, 15(3): 375-393
|
|
|
Phansalkar, V.V., Sastry, P.S. (1994) Analysis of the back-propagation algorithm with momentum. IEEE Transactions on Neural Networks, 5(3): 505-506
|
|
|
Powell, M. J. D. (1977) Restart procedures for the conjugate gradient method. Mathematical Programming, 12(1): 241-254
|
|
3
|
Riedmiller, M., Braun, H. (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. u: IEEE International Conference on Neural Networks, Institute of Electrical and Electronics Engineers (IEEE), str. 586-591
|
|
|
Salazar-Tejeda, P.A., Melin, P., Castillo, O. (2008) A New Biometric Recognition Technique Based on Hand Geometry and Voice Using Neural Networks and Fuzzy Logic. u: Castillo, Oscar; Melin, Patricia; Kacprzyk, Janusz; Pedrycz, Witold [ur.] Soft Computing for Hybrid Intelligent Systems, Berlin, Heidelberg: Springer Nature, str. 171-186
|
|
|
Wilde, P. (1997) The Magnitude of the Diagonal Elements in Neural Networks. Neural Networks, 10(3): 499-504
|
|
|
Yam, J.Y.F., Chow, T.W.S. (2000) A weight initialization method for improving training speed in feedforward neural network. Neurocomputing, 30(1-4): 219-232
|
|
|
Yeung, D.S., Chan, P.P.K., Ng, W.W.Y. (2009) Radial Basis Function network learning using localized generalization error bound. Information Sciences, 179(19): 3199-3217
|
|
|
|
|