Agent-based Centralized Fuzzy Kalman Filtering for Uncertain Stochastic Estimation

被引：0

作者：

Tatari, Farzaneh ^{[1
]}

Akbarzadeh-T, Mohammad-R ^{[1
]}

Mazouchi, Majid ^{[1
]}

Javid, Gelareh ^{[1
]}

机构：

[1] Ferdowsi Univ Mashhad, Dept Elect Engn, Mashhad, Iran

来源：

2009 FIFTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTIONS IN SYSTEM ANALYSIS, DECISION AND CONTROL | 2010年

关键词：

agent-based sensor network; centralized fuzzy Kalman filtering; fuzzy Kalman filter; possibility; result sharing;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we investigate the problem of agent-based centralized fuzzy Kalman fillers observing an uncertain physical process with parametric uncertainties. An agent-based sensor network is a distributed system which consists of sensors with limited computational capabilities. In our agent-based sensor network we consider sensor agents and a moderator agent. Any of these sensor agents have limited computational capabilities and also may be affected by different noises. Agents derive the information in the form of fuzzy states from their fuzzy Kalman filters, the estimated fuzzy states would be transmitted to the moderator agent for aggregation and result sharing by any sensor agent. The moderator agent fuses the fuzzy estimations to generate the global state estimations which is highly reliable.

引用

页码：55 / 58

页数：4

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