根据经典径向基函数(RBF)神经网络的优势，结合星图模式样本集的特点，设计了一种适合星图模式样本的网络训练算法。从提取星图模式入手，引入三角剖分理论，将可能出现在同一视场内的恒星以三角形的形式连接起来，提取连接的角距作为星图模式，建立了具有完备性、平移旋转不变性的星图模式样本集。然后，利用RBF神经网络做星图识别，研究顺序训练方法和批量训练方法，总结多种经典算法的优缺点，并设计了一种训练方法。通过实验证明了该种方法较其他经典算法更为适合学习星图模式样本。最后，给出RBF神经网络相关的训练数据，并通过模拟星图软件获得若干模拟星图作为观测样本，利用已经训练好的神经网络进行识别。试验结果表明，测试网络能够正确识别这些星图。

A network training method for star pattern recognition was designed by combining a classific Radial Basic Function(RBF) neural network and star pattern samples. Firstly,the star pattern abstraction method was discussed and a triangulation based on star magnitudes was induced to connect the stars which probably appear in the same field of view.By taking extrated angular distances as the characteristic of star pattern, a star pattern sample set with completion, translation and rotation invariance was established. Then, RBF neural network was studied to recognize the star patterns. RBF network training method was classified as sequence learning and batch learning. Some typical algorithms that could represent the two methods were studied on their advantages and disadvantages,and a new training method was designed based on the specialty of above star pattern sample sets.Experiments indicate that the designed method is more appropriate than those typical algorithms. Several star images were simulated through software, which was regarded as the observatory data and entered into the trained RBF neural network to test. The experiment results show that the network can recognize all the star patterns successfully.