A Neural Net Model for Prediction
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[1] A. Lapedes,et al. Nonlinear Signal Processing Using Neural Networks , 1987 .
[2] M. B. Priestley,et al. Non-linear and non-stationary time series analysis , 1990 .
[3] T Poggio,et al. Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.
[4] R. Palmer,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[5] Richard J. Meinhold,et al. Robustification of Kalman Filter Models , 1989 .
[6] R. Berk,et al. Limiting Behavior of Posterior Distributions when the Model is Incorrect , 1966 .
[7] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[8] Martin Casdagli,et al. Nonlinear prediction of chaotic time series , 1989 .
[9] H. Tong,et al. Threshold Autoregression, Limit Cycles and Cyclical Data , 1980 .
[10] Masanao Aoki,et al. State Space Modeling of Time Series , 1987 .
[11] M. Priestley. STATE‐DEPENDENT MODELS: A GENERAL APPROACH TO NON‐LINEAR TIME SERIES ANALYSIS , 1980 .
[12] P. S. Lewis,et al. Function approximation and time series prediction with neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[13] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[14] M. West,et al. Dynamic Generalized Linear Models and Bayesian Forecasting , 1985 .
[15] Farmer,et al. Predicting chaotic time series. , 1987, Physical review letters.
[16] James L. McClelland,et al. James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.
[17] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[18] P. D. Jong. The Diffuse Kalman Filter , 1991 .
[19] J. Doyne Farmer,et al. Exploiting Chaos to Predict the Future and Reduce Noise , 1989 .
[20] Martin Casdagli,et al. An analytic approach to practical state space reconstruction , 1992 .
[21] H. White. Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models , 1989 .