Research on the effectiveness evaluation of big data in combat simulation

被引：2

作者：

Hu, Xinwu ^{[1
]}

Luo, Pengcheng ^{[2
]}

Zhang, Xiaonan ^{[1
]}

Wang, Jun ^{[1
]}

Zhou, Tianren ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China

[2] AllSim Technol Inc, Changsha, Hunan, Peoples R China

来源：

PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON BIG DATA RESEARCH (ICBDR 2018) | 2018年

关键词：

neural network; effectiveness evaluation; big data; system simulation; data dimension reduction;

D O I：

10.1145/3291801.3291827

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

With the development of complex system simulation techniques, computational capabilities, and data management capabilities, the simulation results tend to be big data. There are also high-dimension, high-redundancy, and high-correlation issues among indexes. Based on the above background, a two-layer Autoencoder neural network is used for feature extraction and dimensionality reduction. Then, 10 deep neural network models are established for index learning. The experimental results show that the 32-layer Resent network works best for low-dimensional data effectiveness evaluation.

引用

页码：70 / 75

页数：6

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