Chained DLS-ICBP Neural Networks with Multiple Steps Time Series Prediction

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.