关于微博平台特征信息优化检测仿真研究

发布时间：2018-07-06 10:59

  本文选题：微博平台 + 特征信息　； 参考：《计算机仿真》2017年05期

【摘要】：对微博平台特征信息优化检测的研究,可对海量微博信息中所需信息进行高效检索。对特征信息优化检测的过程,需要对信息重排,并进行主成分特征提取,完成对特征信息的检测。传统方法结合相关性检验,对样本信息流进行处理和分析,但忽略了对信息的主成分进行特征提取,导致检测精度偏低。提出基于萤火虫优化神经网络的微博平台特征信息优化检测仿真。采用自回归移动平均模型对统计得到的微博平台特征信息进行信息重排,对重排的微博特征信息信息流采用神经网络训练方法进行主成分特征提取,对提取的主成分特征采用优化的萤火虫算法进行特征筛选和自组织监督学习,实现微博平台信息的优化检测。仿真结果表明,采用上述方法进行微博信息准确检测准确度较高,需要的先验样本知识相对较小,检测的可靠度得到保证。
[Abstract]:The research of Weibo platform feature information optimization detection can efficiently retrieve the information needed in massive Weibo information. In the process of optimizing the detection of feature information, we need to rearrange the information and extract the principal component feature to complete the detection of feature information. The traditional method combines the correlation test to process and analyze the sample information flow, but neglects the feature extraction of the principal components of the information, which leads to the low detection accuracy. Based on firefly optimization neural network, the simulation of Weibo platform feature information optimization detection is proposed. The autoregressive moving average model is used to rearrange the feature information of the Weibo platform, and the neural network training method is used to extract the principal component feature of the rearranged Weibo feature information flow. The extracted principal component features are selected by the optimized firefly algorithm and self-organized supervised learning to realize the optimal detection of Weibo platform information. The simulation results show that the accuracy of accurate detection of Weibo information by using the above method is high, the knowledge of prior samples is relatively small, and the reliability of detection is guaranteed.
【作者单位】： 常州大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61272367)
【分类号】：TP18;TP393.09
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本文编号：2102563