Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks-Reference-Cited by-同舟云学术

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

Published:2014-08-26 Issue:8 Volume:9 Page:e105766
ISSN:1932-6203
Container-title:PLoS ONE
language:en
Short-container-title:PLoS ONE

Author:

Al-Jumeily Dhiya,Ghazali Rozaida,Hussain Abir

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference43 articles.

1. Linear prediction: A tutorial review;J Makhoul;Proceedings of the IEEE,1975

2. Recurrent neural networks and time series prediction;J Conner;IEEE International Joint conference on Neural network,1991

3. Rape R, Fefer D, Drnovsek J (1994) Time series prediction with neural networks: a case of two examples. IEEE Instrumentation and measurement technology conference: 145–148.

4. Fuzzy Nearest Neighbour Method for Time-Series Forecasting;S Singh;Proc 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT',1998

5. A forecasting method for time series with fractal geometry and its application;S Tokinga;Electronic and communications in Japan part 3,1999

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