基于卷积神经网络的Gray-Scott模型图像分类算法

Image classification algorithm of Gray-Scott model based on convolutional neural

利用Gray-Scott反应扩散模型讨论图灵斑图的分类算法。利用线性稳定性理论分析了系统的共存平衡点,在模型中加入扩散项会产生不同类型的斑图。使用单层卷积神经网络代替多层算法特征工程来对生成的斑图进行分类。使用与特征X相关的数据特征X-X^(3)和▽X来表示复杂图案数据的界面和块状区域。在神经网络和聚类算法中使用X-X^(3)和▽X进行分类。数值结果验证了该方法的有效性。

The classification algorithm of Turing pattern with the help of Gray-Scott reaction-diffusion model was investigated.The linear stability theory was used to analyze the coexistence balance point of the system and ensure the different types of patterns produced by Turing instability.Convolutional neural networks instead of multi-layer algorithm feature engineering were adopted to classify the generated patterns.Data features X-X^(3)and ▽X that are more meaningful than X was used to represent the interface and main area of complex pattern data.Therefore,X-X^(3)and ▽X were employed to realize the classification in neural networks and clustering algorithms.It was found that the effectiveness of the method was verified by the results.