BACK-PROPAGATION, WEIGHT-ELIMINATION AND TIME SERIES PREDICTION

[1]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[2]  P. Foukal.  The variable sun. , 1990 .

[3]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[4]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[5]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[6]  J. Doyne Farmer,et al.  Exploiting Chaos to Predict the Future and Reduce Noise , 1989 .

[7]  Geoffrey E. Hinton,et al.  Dimensionality Reduction and Prior Knowledge in E-Set Recognition , 1989, NIPS.

[8]  Hervé Bourlard,et al.  Generalization and Parameter Estimation in Feedforward Netws: Some Experiments , 1989, NIPS.

[9]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[10]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[11]  Neil Gershenfeld,et al.  An Experimentalist’s Introduction to the Observation of Dynamical Systems , 1988 .

[12]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[13]  Lorien Y. Pratt,et al.  Comparing Biases for Minimal Network Construction with Back-Propagation , 1988, NIPS.

[14]  Lawrence D. Jackel,et al.  Large Automatic Learning, Rule Extraction, and Generalization , 1987, Complex Syst..

[15]  D. Robertson,et al.  Studying the earth by very-long-baseline interferometry , 1986 .

[16]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[17]  H. Tong,et al.  Threshold Autoregression, Limit Cycles and Cyclical Data , 1980 .