Uncertainty-complexity trade-offs for sensor compensation design

被引：0

作者：

Gubian, A. ^{[1
]}

Marconato, A. ^{[1
]}

Boni, A. ^{[1
]}

Petri, D. ^{[1
]}

机构：

[1] Univ Trent, Dept Informat & Commun Teachnol, Via Sommarive 14, I-38050 Trento, Italy

来源：

2007 IEEE INTERNATIONAL WORKSHOP ON ADVANCED METHODS FOR UNCERTAINTY ESTIMATION IN MEASUREMENT | 2007年

关键词：

sensor compensation; support vector machines; multiobjective optimization;

D O I：

10.1109/AMUEM.2007.4362584

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 work we focus on the design of reduced-complexity sensor compensation modules based on learning-from-examples techniques. A Multi-Objective Optimization design framework is proposed, where system complexity and compensation uncertainty are considered as two conflicting costs to be jointly minimized. In addition, suitable statistical techniques are applied to cope with the variability in the uncertainty estimation arising from the limited availability of data at design time. Experimental results on a synthetic benchmark are provided to show the validity of the proposed methodology.

引用

页码：127 / +

页数：3

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