Solving inverse problems using Particle Swarm Optimization: An application to aircraft fuel measurement considering sensor failure

被引：2

作者：

Hu, Kai ^{[1
]}

Huang, Samuel H. ^{[1
]}

机构：

[1] Univ Cincinnati, Dept Mech Ind & Nucl Engn, Intelligent Syst Lab, Cincinnati, OH 45221 USA

来源：

INTELLIGENT DATA ANALYSIS | 2007年 / 11卷 / 04期

关键词：

inverse problem; Particle Swarm Optimization; neural networks; aircraft fuel measurement; missing data;

D O I：

10.3233/IDA-2007-11407

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a robust modeling method to handle inverse problems with missing data. The modeling method is applied to aircraft fuel measurement considering sensor failure. Neural Networks that are tolerant to noisy data are adapted to approximate the nonlinear physical process. Unlike previous algorithms that use gradient information to search input space in inverse problems, the proposed method thoroughly explores the input space using particle swarm optimization. The comparison results show the effectiveness of our method in dealing with missing data.

引用

页码：421 / 434

页数：14

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