辨识器采用RBF神经网络结构和最近邻聚类算法，实现了对系统逆动力学模型的动态辨识。
The system identifier based on RBF neural network which applies nearest neighbor clustering algorithm realizes the identification of the inverse dynamic system model.
该模型首先采用改进的最近邻聚类算法确定径向基函数中心，接着应用递推最小二乘法训练网络的权值。
The model USES an improved nearest-neighbor clustering algorithm to select the RBF center, and a recursive least square algorithm to train weights of the RBF neural network.
基于一种用于混合属性数据的距离定义和改进的最近邻分类方法，提出了一种基于聚类的有指导的入侵检测方法。
A clustering-based and supervised intrusion detection method was proposed with new distance definition for mixed-attribute data and improved nearest neighbor classification method.
基于一种用于混合属性数据的距离定义和改进的最近邻分类方法，提出了一种基于聚类的有指导的入侵检测方法。
A clustering-based and supervised intrusion detection method was proposed with new distance definition for mixed-attribute data and improved nearest neighbor classification method.
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