Research on trend prediction of internet user intention understanding and public intelligence mining based on fractional differential method

Shaofei Wu, Qian Zhang, Wenting Chen, Jun Liu and Lizhi Liiu

Chaos, Solitons & Fractals, 2019, vol. 128, issue C, 331-338

Abstract: With the rapid development of Internet technology, network users and data are also rapidly accumulating, which leads to significant asymmetry between user needs and massive information. Therefore, how to perceive user intentions and provide users with personalized and accurate information becomes an important research topic; at the same time, the Internet has become another important source of Public Intelligence, widely used in political, economic, military and other fields, people need to explore a new Internet-based public intelligence collection and processing technology. Based on fractional order differential, a new model for predicting user intentions and Public Intelligence mining trends is constructed. The order of the fractional differential equation model is identified by the optimization algorithm. The second and the parameters give the analytical solution of the model, and the resulting prediction equation can be used as a new method for Internet user intention and public intelligence mining prediction. The verification results of real experimental data show that the method can identify the model parameters more accurately, and the obtained fractional differential equation model is more consistent with the actual data.

Keywords: User intention understanding; Public intelligence mining; Fractional differential method; Trend prediction (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077919302875
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:128:y:2019:i:c:p:331-338

DOI: 10.1016/j.chaos.2019.07.034

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().