Abstract
Based on the circular back-propagation (CBP) network, the improved circular back-propagation (ICBP) neural network was previously put forward and exhibits more general architecture than the former. It has a favorable characteristic that ICBP is better than CBP in generalization and adaptation though the number of its adaptable weights is generally less than that of CBP. The forecasting experiments on chaotic time series, multiple-input multiple-output (MIMO) systems and the data sets of daily life water consumed quantity have proved that ICBP has better capabilities of prediction and approximation than CBP. But in the above predicting process, ICBP neglects inherent structural changes and time correlation in time series themselves. In other words, they do not take into account the influence of different distances between observations and the predicting point on forecasting performance. The principle of discounted least-square (DLS) formulates this influence exactly. In this paper, the DLS principle is borrowed to construct the learning algorithm of DLS-ICBP. On this basis we construct chained DLS-ICBP neural networks by combining a new kind of chain structure to DLS-ICBP and investigate multiple steps time series prediction. We prove that DLS-ICBP has better single and multiple step predictive capabilities than ICBP through experiments on the data sets of Benchmarks and water consumed quantity.
Similar content being viewed by others
References
Haykin, S.: Neural Networks: A Comprehensive Foundation, Second Edition, Tshinghua University Press, (2001),10: 156–252. ISBN 7-302-04936-X/TP·2778.
S. Ridolla S. Rovetta R. Zunino (1997) ArticleTitleCircular back-propagation networks for classification IEEE Transactions on Neural Networks 8 IssueID1 84–97
Ridolla, S. Rovetta, S. and Zunino, R.: CBP networks as a generalized neural model, In: International Conference on Neural Networks, 1997.
Ridolla, S. Rovetta, S. and Zunino, R.: Circular backpropagation networks embed vector quantization, IEEE Transaction on Neural Networks, 10(4), (1999).
Z. B. Zhu C. S. Can (2001) ArticleTitleEquivalence between vector quantization and ICBP networks Journal of Data Acquisition and Processing 16 IssueID3 291–294
Zhu, Z. B.: The research on the performance and applications of improved BP neural networks, Thesis of Master degree (in Chinese), Nanjing University of Aeronautics and Astronautics, 2001, p. 2.
Q. Dai S. Chen Z.B. Zhu (2003) ArticleTitleImproved CBP neural networks with its applications in time series prediction Neural Processing Letters 18 IssueID3 217–231
A.-P. Refenes Y. Bentz D. W. Bunn (1997) ArticleTitleFinancial time series modelling with discounted least squares backpropagation Neurocomputing 14 123–138
Vapnik, V. N.: The essence of statistical learning theory (in Chinese), Tsinghua University, 2000, Translated by Zhang Xue Gong.
C. M. Bishop (1995) Neural Networks for Pattern Recognition. Oxford University Press Oxford
M. Duhoux J. Suykens B. De Moor J. Vandewalle (2001) ArticleTitleImproved long-term temperature prediction by chaining of neural networks International Journal of Neural Systems 11 IssueID1 1–10
J. Suykens J. Vandewalle (1998) Nonlinear Modeling: Advanced Black Box Techniques Kluwer Academic Publishers Boston
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dai, Q., Chen, S. Chained DLS-ICBP Neural Networks with Multiple Steps Time Series Prediction. Neural Process Lett 21, 95–107 (2005). https://doi.org/10.1007/s11063-004-7774-7
Issue Date:
DOI: https://doi.org/10.1007/s11063-004-7774-7