Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Improved CBP Neural Network Model with Applications in Time Series Prediction
Neural Processing Letters
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
Circular backpropagation networks embed vector quantization
IEEE Transactions on Neural Networks
Prediction error feedback for time series prediction: a way to improve the accuracy of predictions
ECC'10 Proceedings of the 4th conference on European computing conference
WSEAS Transactions on Systems and Control
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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.