Application of Bayesian Decision Theory Based on Prior Information in the Multi-Objective Optimization Problem

DOI

10.2991/ijcis.2010.3.s1.3How to use a DOI?

Keywords

Multi-Objective Optimization, Prior Information, Maximum Entropy Principle, Distributed multiple inputs and multiple outputs.

Abstract

General multi-objective optimization methods are hard to obtain prior information, how to utilize prior information has been a challenge. This paper analyzes the characteristics of Bayesian decision-making based on maximum entropy principle and prior information, especially in case that how to effectively improve decision-making reliability in deficiency of reference samples. The paper exhibits effectiveness of the proposed method using the real application of multi-frequency offset estimation in distributed multiple-input multiple-output system. The simulation results demonstrate Bayesian decision-making based on prior information has better global searching capability when sampling data is deficient.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xia Lei
AU  - Maozhu Jin
AU  - Qiang Wang
PY  - 2010
DA  - 2010/12/01
TI  - Application of Bayesian Decision Theory Based on Prior Information in the Multi-Objective Optimization Problem
JO  - International Journal of Computational Intelligence Systems
SP  - 31
EP  - 42
VL  - 3
IS  - Supplement 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.s1.3
DO  - 10.2991/ijcis.2010.3.s1.3
ID  - Lei2010
ER  -