人工神经网络在天文学中的应用

The Application of Artificial Neural Network in Astronomy

人工神经网络具有自学习、自适应、鲁棒性好、动态响应快等特点,并具有较强的非线性处理问题能力,因此在天文学中得到广泛而成功的应用．综述了人工神经网络在天文学中主要应用模型的基本原理和优缺点,阐述了人工神经网络适用于天文学的某些基本特征,着重介绍了人工神经网络在天文学中的具体应用实例,并对其发展和应用前景进行了展望．由于天文数据分布的庞杂和天文数据量的急剧增加,人工神经网络将日益显示出优越性．

Artificial Neural Netwoks （ANNs） are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds： supervised and unsupervised. The supervised approach lays the stress on ＂teachin＂ a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful wherl the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness capability of dealing with non-linear problems. interest toward the astronomical applications and dynamically rapid response as well as strong In the last few years there has been an increasing of ANNs. In this paper, we firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network topologies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. We summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. We here focus on some of the most interesting fields of its application, for example： object detection, star/galaxy clas- sification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been only touched upon. Finally, we discuss the development and application prospects of ANNs. With the increase of quantity and the distributing complexity of astronomical data, its scientific exploitation requires a variety of automated tools, which are capable to perform huge amount of work, such as data preprocessing, feature selection, data reduction, data mining and data analysis. ANNs, one of intelligent tools, will show more and more superiorities.