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Mathematical Problems in Engineering
Volume 2015, Article ID 931629, 8 pages
Research Article

Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks

1Department of Computer Science & Technology, East China Normal University, Shanghai 200241, China
2Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
3Guangxi Climate Center, Guangxi Meteorological Bureau, Nanning 530022, China

Received 27 August 2014; Accepted 20 December 2014

Academic Editor: Cagdas Hakan Aladag

Copyright © 2015 Jian Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


When pure linear neural network (PLNN) is used to predict tropical cyclone tracks (TCTs) in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1) and (0, 1); with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1) and (100, 1). We present the following results: (1) data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2) mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.