Ant Colony Estimator: An intelligent particle filter based on ACOR

被引：27

作者：

Heris, S. Mostapha Kalami ^{[1
]}

Khaloozadeh, Hamid ^{[1
]}

机构：

[1] KN Toosi Univ Technol, Ind Control Ctr Excellence, Dept Syst & Control Engn, Fac Elect & Comp Engn, Tehran, Iran

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2014年 / 28卷

关键词：

Particle filter (PF); Ant colony optimization (ACO); Intelligent filtering (IF); Evolutionary filtering (EF); Nonlinear filtering; STATE ESTIMATION; HIV THERAPY; TRACKING; OPTIMIZATION; STRATEGIES; SYSTEM; ADAPTATION; DYNAMICS; TARGETS; MODELS;

D O I：

10.1016/j.engappai.2013.11.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Based on Ant Colony Optimization for Continuous Domains (ACO(R)) and Particle Filter (PF), an intelligent particle filter, namely Ant Colony Estimator (ACE), is proposed in this paper. Modeling and search abilities of ACO(R) are incorporated into the standard particle filtering framework to improve the estimation performance and overcome the well-known problems of Degeneracy and Sample Impoverishment. ACO(R) operators implicitly use measurement and previous particle information, to generate probably better particles. Simulation results are given for two examples and ACE is compared to other types of particle filters. The obtained results confirm the efficiency and applicability of ACE. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：78 / 85

页数：8

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