NDLI: Prediction model of sintering burden based on information entropy and chaos PSO algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ling Qin
Copyright Year	2012
Description	Author affiliation: School of Electrical and Electronic Engineering, Wuhan Polytechnic University, China (Ling Qin)
Abstract	Considering the characteristics of the nonlinear, complexity and relativity of the sintering burden system, the prediction model of sintering burden is established by BP neural network. In addition, a new optimization method of the sintering experiment is proposed, based on information entropy and chaotic improved particle swarm algorithm. The initial particle colony is produced by information entropy to increase the variety of the initial colony. The strategy of dynamic nonlinear adjustment is used for the inertia weight in this paper according to the iteration times, so as to improve the algorithm's searching capability. And the traversal characteristic of chaos optimization is introduced to overcome effectively the local convergence of standard particle swarm algorithm. The simulating results show that the improved particle swarm algorithm has faster converge, fewer iteration times and stronger global optimization ability.
Starting Page	2566
Ending Page	2569
File Size	234723
Page Count	4
File Format	PDF
ISBN	9781467313971
e-ISBN	9781467313988
DOI	10.1109/WCICA.2012.6358305
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Optimization Information entropy Chaos Prediction algorithms Educational institutions Heuristic algorithms chaotic mutation sintering burden PSO algorithm information entropy inertia weight
Content Type	Text
Resource Type	Article

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