天体物理实验中的自组织神经网络自动分类
Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments
摘要：
自组织映射（SOM）是解决分类问题的有效工具，近年来发展了一种更强大的动态增长神经网络。自动分类（也称聚类）是许多天体物理实验中的一个重要而又困难的问题，如伽玛射线暴分类、伽玛强子分离等。在简单介绍了分类问题之后，我们将在第2节讨论自组织映射。第3节讨论了生长神经网络的各种模型，最后在第4节中，我们讨论了生长神经网络在天体物理问题中的有效分类的研究前景。
英文摘要：
Self-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an important and difficult problem in many Astrophysical experiments, for instance, Gamma Ray Burst classification, or gamma-hadron separation. After a brief introduction to classification problem, we discuss Self-Organising Maps in section 2. Section 3 discusses with various models of growing neural networks and finally in section 4 we discuss the research perspectives in growing neural networks for efficient classification in astrophysical problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
