A neural-FIR predictor: Minimum size estimation based on nonlinearity analysis of input sequence

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1327, Issue , 1997, Pages 1047-1052

  Khalaf, Ashraf A M ^a   Nakayama, Kenji ^a   Hara, Kazuyuki ^b  

^a KANAZAWA UNIVERSITY   (Japan)

^b Gunma Polytechnic College   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FIR FILTERS; IMPULSE RESPONSE; NETWORK LAYERS; TIME SERIES; TIME SERIES ANALYSIS;

COMPUTER GENERATED; FASTER CONVERGENCE; HIDDEN NEURONS; NONLINEAR PREDICTORS; NONLINEAR TIME SERIES; NONLINEARITY ANALYSIS; RESIDUAL ERROR; SIZE ESTIMATION;

MULTILAYER NEURAL NETWORKS;

EID: 84956611057     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0020291     Document Type: Conference Paper

References (6)

1. A. S. Weigend and D. E. Rurnrlhart: Generalization through Minimal Networks with Application to Forecasting. Proceeding of INTERFACE: Computing Science and Statistics, edited by Elaine Keramindas. Springer Verlag (1992) 362-370
2. Kinouchi, M. and Hagiwara, M.: Learning Temporal Sequences by Complex Neurons with Local Feedback. ICNN 6 (1995) 3165-3169
3. Haykin L. Li: Nonlinear Adaptive Prediction of Nonstationary Signals. IEEE Trans. Signal Processing, 43 (1995) 526-535
4. A. S. Weigend and N. A. Gershenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past. Proceedings 5. XV, Santa Fe Institute (1994)
5. Tong, Howell and Lim, K. S. : Threshold Autoregression, Limit Cycles and Cyclical Data. Journal Royal Statistical Society B 42, (1980) 245-292
6. Ashraf A.M. Khalaf, and K. Nakayama: A Hybrid Neural Predictor and Its Convergence Analysis. Proceeding of the 10th Karuizawa Workshop on Circuits and Systems, Japan (1997) 357-362