Toggle navigation
Home
About
About Journal
Historical Evolution
Indexed In
Awards
Reference Index
Editorial Board
Journal Online
Archive
Project Articles
Most Download Articles
Most Read Articles
Instruction
Contribution Column
Author Guidelines
Template
FAQ
Copyright Agreement
Expenses
Academic Integrity
Contact
Contact Us
Location Map
Subscription
Advertisement
中文
Journals
Publication Years
Keywords
Search within results
(((SONG Yafei[Author]) AND 1[Journal]) AND year[Order])
AND
OR
NOT
Title
Author
Institution
Keyword
Abstract
PACS
DOI
Please wait a minute...
For Selected:
Download Citations
EndNote
Ris
BibTeX
Toggle Thumbnails
Select
Network security situation prediction based on improved particle swarm optimization and extreme learning machine
TANG Yanqiang, LI Chenghai, SONG Yafei
Journal of Computer Applications 2021, 41 (
3
): 768-773. DOI:
10.11772/j.issn.1001-9081.2020060924
Abstract
(
383
)
PDF
(1076KB)(
624
)
Knowledge map
Save
Focusing on the problems of low prediction accuracy and slow convergence speed of network security situation prediction model, a prediction method based on Improved Particle Swarm Optimization Extreme Learning Machine (IPSO-ELM) algorithm was proposed. Firstly, the inertia weight and learning factor of Particle Swarm Optimization (PSO) algorithm were improved to realize the adaptive adjustment of the two parameters with the increase of iteration times, so that PSO had a large search range and fast speed at the initial stage, strong convergence ability and stability at the later stage. Secondly, aiming at the problem that PSO is easy to fall into the local optimum, a particle stagnation disturbance strategy was proposed to re-guide the particles trapped in the local optimum to the global optimal flying. The Improved Particle Swarm Optimization (IPSO) algorithm obtained in this way ensured the global optimization ability and enhanced the local search ability. Finally, IPSO was combined with Extreme Learning Machine (ELM) to optimize the initial weights and thresholds of ELM. Compared with ELM, the ELM combining with IPSO had the prediction accuracy improved by 44.25%. Experimental results show that, compared with PSO-ELM, IPSO-ELM has the fitting degree of prediction results reached 0.99, and the convergence rate increased by 47.43%. The proposed algorithm is obviously better than the comparison algorithms in the prediction accuracy and convergence speed.
Reference
|
Related Articles
|
Metrics
Select
Temporal evidence fusion method with consideration of time sequence preference of decision maker
LI Xufeng, SONG Yafei, LI Xiaonan
Journal of Computer Applications 2019, 39 (
6
): 1626-1631. DOI:
10.11772/j.issn.1001-9081.2018102218
Abstract
(
358
)
PDF
(873KB)(
206
)
Knowledge map
Save
Aiming at temporal uncertain information fusion problem, to fully reflect the dynamic characteristic and the influence of time factor on temporal information fusion, a temporal evidence fusion method was proposed with considering decision maker's preference for time sequence based on evidence theory. Firstly, time sequence preference of decision maker was fused to temporal evidence fusion, through the analysis of characteristics of temporal evidence sequence, decision maker's preference for time sequence was measured based on the definition of temporal memory factor. Then, the evidence source was revised by time sequence weight vector obtained by constructing the optimal model and evidence credibility idea. Finally, the revised evidences were fused by Dempster combination rule. Numerical examples show that compared with other fusion methods without considering time factor, the proposed method can deal with conflicting information in temporal information sequence effectively and obtain a reasonable fusion effect; meanwhile, with the consideration of the credibility of temporal evidence sequence and the subjective preference of decision maker, the proposed method can reflect the influence of subjective factors of decision maker on temporal evidence fusion, giving a good expression to the dynamic characteristic of temporal evidence fusion.
Reference
|
Related Articles
|
Metrics