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Article

A Neural Network-Based Interval Pattern Matcher

by 1,2, 1, 3,4,* and 5,6
1
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Shanxi Meteorological Administration, Taiyuan 030006, China
3
National Meteorological Center of China Meteorological Administration, Beijing 100081, China
4
Environmental Modeling Center, NOAA/NWS/National Centers for Environmental Prediction, College Park, MD 20740, USA
5
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
6
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2015, 6(3), 388-398; https://doi.org/10.3390/info6030388
Received: 7 May 2015 / Revised: 13 July 2015 / Accepted: 14 July 2015 / Published: 17 July 2015
One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising. View Full-Text
Keywords: neural network; interval; pattern matcher; weather forecasting neural network; interval; pattern matcher; weather forecasting
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MDPI and ACS Style

Lu, J.; Xue, S.; Zhang, X.; Han, Y. A Neural Network-Based Interval Pattern Matcher. Information 2015, 6, 388-398. https://doi.org/10.3390/info6030388

AMA Style

Lu J, Xue S, Zhang X, Han Y. A Neural Network-Based Interval Pattern Matcher. Information. 2015; 6(3):388-398. https://doi.org/10.3390/info6030388

Chicago/Turabian Style

Lu, Jing, Shengjun Xue, Xiakun Zhang, and Yang Han. 2015. "A Neural Network-Based Interval Pattern Matcher" Information 6, no. 3: 388-398. https://doi.org/10.3390/info6030388

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