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

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 applie...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-8
Main Authors: Jin, Jian, Jin, Long, Li, Ming
Format: Article
Language:English
Subjects:
Bias
Cyclones
Datasets
Experiments
Intervals
Mapping
Mathematical models
Mathematical problems
Neural networks
Normal distribution
Nuclear power plants
Standard deviation
Time series
Training
Variables
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/931629