Computational Intelligence in Astronomy: A Survey

被引：13
|
作者：
Wang, Ke [1 ]
Guo, Ping [2 ]
Yu, Fusheng [3 ]
Duan, Lingzi [3 ]
Wang, Yuping [4 ]
Du, Hui [5 ]
机构：
[1] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
[2] Beijing Normal Univ, Sch Syst Sci, Image Proc & Pattern Recognit Lab, Beijing 100875, Peoples R China
[3] Beijing Normal Univ, Sch Math Sci, Minist Educ, Lab Math & Complex Syst, Beijing 100875, Peoples R China
[4] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Shaanxi, Peoples R China
[5] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Gansu, Peoples R China
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2018年 / 11卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Computational intelligence; astronomical data analysis; neural networks; fuzzy set; evolutionary computation; DIGITAL SKY SURVEY; AUTOMATED MORPHOLOGICAL CLASSIFICATION; ESTIMATING PHOTOMETRIC REDSHIFTS; STAR-GALAXY DISCRIMINATION; UNIVERSE PULSAR SURVEY; LOGIC-BASED ALGORITHM; STELLAR SPECTRA; NEURAL-NETWORK; 2-DIMENSIONAL CLASSIFICATION; SYSTEM;
D O I：
10.2991/ijcis.11.1.43
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With explosive growth of the astronomical data, astronomy has become a representative data-rich discipline so as to defy traditional research methodologies and paradigm to analyze data and discover new knowledge from the data. How to effectively process and analyze the astronomical data is a fundamental work while a key scientific requirement of modern astronomical surveys. This situation has motivated needs for fostering of a wide range of cooperation with the astronomers and computer scientists. Computational intelligence, an important research direction of artificial intelligence and information sciences, has been shown to be promising to solve complex problems in scientific research and engineering. This paper presents a review of the current state of the application of computational intelligence in astronomy. We believe that computational intelligence is expected to provide powerful tools for addressing challenges in astronomical data analysis.
引用
收藏
页码：575 / 590
页数：16
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