-
3
-
-
9444226104
-
Generalization through minimal networks with application to forecasting
-
Springer Verlag
-
A.S. Weigend and D.E. Rumelhart, Generalization through minimal networks with application to forecasting, Proc. INTERFACE'91: Computing Science and Statistics, ed. Elainc Keramindas, pp.362-370, Springer Verlag, 1992.
-
(1992)
Proc. INTERFACE'91: Computing Science and Statistics, Ed. Elainc Keramindas
, pp. 362-370
-
-
Weigend, A.S.1
Rumelhart, D.E.2
-
4
-
-
0029252331
-
Nonlinear adaptive prediction of nonstationary signals
-
Feb.
-
S. Haykin and L. Li, Nonlinear adaptive prediction of nonstationary signals, IEEE Trans. Signal Processing, vol.43, no.2, pp.526-535, Feb. 1995.
-
(1995)
IEEE Trans. Signal Processing, Vol.43, No.
, pp. 526-535
-
-
Haykin, S.1
Li, L.2
-
6
-
-
0029548014
-
Learning temporal sequences by complex neurons with local feedback
-
M. Kinouchi and M. Hagiwara, Learning temporal sequences by complex neurons with local feedback, Proc. ICNN'95, pp.3165-3169, 1995.
-
(1995)
Proc. ICNN'95
, pp. 3165-3169
-
-
Kinouchi, M.1
Hagiwara, M.2
-
7
-
-
0030702720
-
A hierarchial bayes approach to nonlinear time series prediction with neural nets
-
T. Matsumoto, H. Hamagishi, and Y. Chonan, A hierarchial bayes approach to nonlinear time series prediction with neural nets, Proc. ICNN'97, pp.2028-2033, 1997.
-
(1997)
Proc. ICNN'97
, pp. 2028-2033
-
-
Matsumoto, T.1
Hamagishi, H.2
Chonan, Y.3
-
8
-
-
0030688746
-
Sequential network construction for time series prediction
-
T.J. Cholewo and J.M. Zurada, Sequential network construction for time series prediction, Proc. ICNN'97, pp.2034-2038, 1997.
-
(1997)
Proc. ICNN'97
, pp. 2034-2038
-
-
Cholewo, T.J.1
Zurada, J.M.2
-
9
-
-
0030651076
-
-
A. Atia, N. Talaat, and S. Shaheen An efficient stock market forecasting model using neural networks, Proc. ICNN'97, pp.2112-2115, 1997.
-
(1997)
An Efficient Stock Market Forecasting Model Using Neural Networks, Proc. ICNN'97
, pp. 2112-2115
-
-
Atia, A.1
Talaat, N.2
Shaheen, S.3
-
10
-
-
0031271851
-
Power prediction in mobile communication systems using an optimal neural-network structure
-
Nov.
-
X.M. Gao, X.Z. Gao, J.M.A. Tanskanen, and S.J. Ovaska, Power prediction in mobile communication systems using an optimal neural-network structure, IEEE Trans. Neural Networks, vol.8, no.C, pp.1446-1455, Nov. 1997.
-
(1997)
IEEE Trans. Neural Networks
, vol.8
, pp. 1446-1455
-
-
Gao, X.M.1
Gao, X.Z.2
Tanskanen, J.M.A.3
Ovaska, S.J.4
-
11
-
-
84956611057
-
A neural-FIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence
-
Lausanne, Switzerland, Oct.
-
A.A.M. Khalaf, K. Nakayama, and K. Hara, A neural-FIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence, Proc. ICANN'97, pp.1047-1052, Lausanne, Switzerland, Oct. 1997.
-
(1997)
Proc. ICANN'97
, pp. 1047-1052
-
-
Khalaf, A.A.M.1
Nakayama, K.2
Hara, K.3
-
12
-
-
0032023055
-
A cascade form predictor of neural and FIR filters and its minimum size estimation based on nonlinearity analysis of time series
-
March
-
A.A.M. Khalaf and K. Nakayama, A cascade form predictor of neural and FIR filters and its minimum size estimation based on nonlinearity analysis of time series, IEICE Trans. Fundamental, vol.E81-A, no.3, pp.364-373, March 1998.
-
(1998)
IEICE Trans. Fundamental, Vol.E81-A, No.3
, pp. 364-373
-
-
Khalaf, A.A.M.1
Nakayama, K.2
-
13
-
-
0031639887
-
-
Anchorage, Alaska, May
-
A.A.M. Khalaf and K. Nakayama, ''Time scries prediction using a hybrid model of neural network and FIR filter, Proc. of IJCNN'98, pp.1975-1980, Anchorage, Alaska, May 1998.
-
(1998)
''Time Scries Prediction Using a Hybrid Model of Neural Network and FIR Filter, Proc. of IJCNN'98
, pp. 1975-1980
-
-
Khalaf, A.A.M.1
Nakayama, K.2
-
15
-
-
0000003175
-
Threshold autoregression, limit cycles and cyclical data
-
H. Tong and K.S. Lim, Threshold autoregression, limit cycles and cyclical data, Journal Royal Statistical Society B, vol.42, pp.245-292, 1980.
-
(1980)
Journal Royal Statistical Society B, Vol.
, vol.42
, pp. 245-292
-
-
Tong, H.1
Lim, K.S.2
|