Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

一种基于新型数据预测方法的MICA仿真研究

在多元统计过程监控中,为解决因未知过程数据统计分布而产生误报漏报的现象,提出一种结合多向独立元分析法(MICA)和广义相关系数(GCC)数据预测的综合方法,进行在线监控过程的仿真。MICA分析方法能有效分解各变量的关联关系,且不需考虑建模数据是否符合正态分布,用此方法计算的独立元变量能更好地描述过程的变化规律。为提高预报未来过程故障的能力,提出用广义相关系数法进行数据预测:确定与运行轨迹相似的监控模型库中的轨迹,并使其相应部分承接于运行轨迹之后。现场采集聚氯乙烯聚合过程的数据进行仿真,仿真结果显示:对于在线监控和在线故障诊断方面,这种新型预测方法优于其它传统处理预测问题的方法。