Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

Validation of Dynamic Simulation Models on Uncertainty

Authors
Xiaojun Guo, Shaojing Su
Corresponding Author
Xiaojun Guo
Available Online November 2016.
DOI
10.2991/aiie-16.2016.14How to use a DOI?
Keywords
autonomous underwater vehicles; validation; credibility; motion model
Abstract

Autonomous underwater vehicles (AUV) play an important role in human activities. They are different from of the vehicles other areas (such as air, ground). Movement, communication, target detection all involve complex hydrology and climatic condition. For special application fields it's even more complex. Due to the limitation of experimental conditions, simulation tests are adopted to assist with the increase in the sample size. The credibility of the simulation models need to be evaluated, especially for dynamic models such as control systems. This paper studies the application of several common dynamic model validation methods in the AUV motion simulation model and compares the resolution of the model. It helps much if cluster validation and cross-validate of the evaluation results are adopted.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
10.2991/aiie-16.2016.14
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.14How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Xiaojun Guo
AU  - Shaojing Su
PY  - 2016/11
DA  - 2016/11
TI  - Validation of Dynamic Simulation Models on Uncertainty
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 60
EP  - 63
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.14
DO  - 10.2991/aiie-16.2016.14
ID  - Guo2016/11
ER  -