提出一个层次自组织神经网络模型，并将其应用于基于事件识别的轨迹分布模式学习中。
This paper presents a hierarchical self organizing neural network model and its application in the learning of the trajectory distribution patterns for event recognition.
提出了一种新的动态模糊自组织神经网络模型(TGFCM)，并将其用于文本聚类中。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text clustering.
引入扩散生长型自组织神经网络模型（DGSOM）算法，在深入研究LLE的基础上提出了新的自组织LLE算法并给出理论分析。
Introducing diffusing and growing self-organizing maps (DGSOM), we propose a new algorithm called self-organized LLE and give some theoretical analysis.
提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。
Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
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