Data fusion based on hybrid intelligent optimization

被引：0
|
作者：
Song, Wei [1 ]
Chen, Zhimin [2 ]
Wang, Chaochen [2 ]
机构：
[1] School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
[2] School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
来源：
Journal of Information and Computational Science | 2013年 / 10卷 / 14期
关键词：
Algorithms - Information fusion - Monte Carlo methods - Optimization;
D O I：
10.12733/jics20102185
中图分类号：
学科分类号：
摘要：
To solve the problems of low precision, slow convergence speed and difficult data fusion of standard particle filtering algorithm, a new hybrid intelligent optimization algorithm applicable for data fusion is presented in this paper and will conduce to finding the ideal solution domain by making use of the global convergence of artificial fish swarm and enhancement of fusion precision by guiding particles to move toward the Gaussian area through particle swarm algorithm. Simulation shows that this algorithm can effectively break away from the local optimum, explore the idea particle optimal value and enhance the convergence speed and fusion precision. 1548-7741/Copyright © 2013 Binary Information Press.
引用
收藏
页码：4611 / 4618
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