期刊论文

Xu, Yuebing

英文

Neural Processing Letters

1370-4621

2019

50

2

1173-1189

10.1007/s11063-018-9914-5

This work was supported in part by the National Natural Science Foundation of China (No. 61573299), the Science and Technology Plan Project of Hunan Province (2016TP1020), the Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University (2017IIPAYB04), the Natural Science Foundation of Hunan Province (No. 2017JJ2011), and the Research Project of the Education Department of Hunan Province (No. 17A031).

本校为通讯机构

The prediction of daily water demands is a crucial part of the effective functioning of the water supply system. This work proposed that a continuous deep belief neural network (CDBNN) model based on the chaotic theory should be implemented to predict the daily water demand time series in Zhuzhou, China. CDBNN should initially be used to predict the urban water demand time series. First, the power spectrum and the largest Lyapunov exponent is used to determine the chaotic characteristic of the daily water demand time series. Second, C-C method is utilized to reconstruct the water demand time s...