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[87] Specht DF (1988) Probabilistic neural networks for classification, or associative
memory, Proc. of IEEE Intern. Conf. on Neural Networks, San Diego, 1: 525-532.
[88] Specht DF (1990) Probabilistic neural networks and the polynomial ADALINE as
complementary techniques for classifications. IEEE Trans. on Neural Networks, 1:
111-121.
[89] Sprecher DA (1965) On the Structure of Continuous Functions of Several Variables.
Trans. Amer. Math. Soc. 115:340-355.
[90] Srinivasan D, Liew AC, Chang CS (1994) A neural network short-term load
forecaster. Electric Power Systems Research 28: 227-234.
[91] Stahlberger A and Riedmuller M (1996) Fast network pruning and feature extraction
using the Unit-OBS algorithm. Advances in Neural Information Processing systems
(NIPS'96), Denver.
[92] Stone M (1977) An asymptotic equivalence of choice of model by cross-validation
and Akaike's criterion cross validation. J. of the Royal Statistical Soc. B36:44-47.
[93] Sue CT, Tong LI, and Leou CM (1997) Combination of time series and neural
network for reliability forecasting modelling. J. Chin. Inst. Ind. Eng. 14(4): 419-429.
[94] Tang Z, Almeida de Ch, and Fishwick, PA (1991) Time series forecasting using
neural networks vs. Box-Jenkins methodology. Simulation 57(5): 303-310.
[95] Tiao GC and Tsay RS (1989) Model specification in multivariate time series. J. of the
Royal Statistical Society B 51: 157-213.
[96] Tikhonov AN (1963) On solving incorrectly posed problems and methods of
regularisation. Docklady Akademii Nauk USSR 151: 501-504.
[97] Tseng F-M, Yu H-Ch, and Tzeng G-H (2002) Combining neural network model with
seasonal time series ARIMA model. Technological Forecasting.
[98] Vapnik V (1995) The Nature of Statistical Learning Theory, Springer-Verlag, NY.
[99] Villiers de J and Bernard E (1992) Backpropagation Neural Nets with one and Two
Hidden Layers. IEEE Trans. On Neural Networks : 136-141.
[100] Vogl TP, Mangis JK, Rigler AK, Zink WT and Allcon DL (1988) Accelerating the
convergence of backpropagation method, Biological Cybernetics, vol. 59: 257-263.
[101] Voort VD, Dougherty M, and Watson M. (1996) Combining Kohonen Maps with
ARIMA time series models to forecast traffic flow. Transp. Res. Circ. (Emerg.
Technol.) 4C(5): 307-318.
[102] Wedding II DK and Cios KJ (1996) Time series forecasting by combining RBF
networks certainty factors, and the Box-Jenkins model. Neurocomputing 10: 149-168.
[103] Weigend AS, Rumelhart DE, and Huberman BA (1991) Generalisation by weight-
elimination with application to forecasting. Adv. In Neural Information Processing
Systems, Morgan Kaufmann, San Mateo, CA 3: 875-882.
[104] Werbos P (1990) Backpropagation through time what it does and how to do it, Proc.
of IEEE, 78(10):1550-1560.
[105] Werbos PJ (1974) Beyond Regression: New Tool for Prediction and analysis in the
Behavioural sciences. Ph.D. Thesis, Harvard University, Cambridge, MA.
[106] Werbos PJ (1989) Backpropagation and neural control: A review and prospectus.
Internat. Joint Conf. of Neural Networks, Washington, 1: 209-216.
[107] Widrow B and Hoff ME (1960) Adaptive Switching Circuits. In: Anderson J and
Rosenfeld E. (eds.) Neurocomputing. MIT Press, Cambridge, MA, 126-134.
[108] Williams RJ and Zipser D (1989) A learning algorithm for continually running fully
recurrent neural networks. Neural Computation 1: 270-280.
[109] Winkler R and Makridakis S (1983) The combination of forecasts, Journal of the
Royal Statistical Society, Series A: 150-157.
[110] Yang Y (2000) Combining different procedures for adaptive regression, J. of
Multivar. Analysis, 74: 135-161.
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