Dependent-Chance Programming on Sugeno Measure Space
Publication type: Journal Article
Publication date: 2017-07-11
SJR: —
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ISSN: 21955468
General Medicine
Abstract
In order to solve the optimization problem of selecting the decision with maximal chance to meet the Sugeno event in Sugeno environment, dependent-chance programming on Sugeno measure space is proposed, which can be considered as a generalized extension of the stochastic dependent-chance programming. Firstly, the theoretical framework of dependent-chance programming on Sugeno measure space is established. Secondly, a Sugeno simulation-based hybrid approach, which consists of back propagation neural network and genetic algorithm, is presented to construct an approximate solution of the complex dependent-chance programming models on Sugeno measure space. Finally, some numerical examples are given to illustrate the effectiveness of the approach.
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Zhang H., Song J. Dependent-Chance Programming on Sugeno Measure Space // Journal of Uncertainty Analysis and Applications. 2017. Vol. 5. No. 1. 7
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Zhang H., Song J. Dependent-Chance Programming on Sugeno Measure Space // Journal of Uncertainty Analysis and Applications. 2017. Vol. 5. No. 1. 7
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TY - JOUR
DO - 10.1186/s40467-017-0061-8
UR - https://doi.org/10.1186/s40467-017-0061-8
TI - Dependent-Chance Programming on Sugeno Measure Space
T2 - Journal of Uncertainty Analysis and Applications
AU - Zhang, Hong
AU - Song, Jianwei
PY - 2017
DA - 2017/07/11
PB - Springer Nature
IS - 1
VL - 5
SN - 2195-5468
ER -
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@article{2017_Zhang,
author = {Hong Zhang and Jianwei Song},
title = {Dependent-Chance Programming on Sugeno Measure Space},
journal = {Journal of Uncertainty Analysis and Applications},
year = {2017},
volume = {5},
publisher = {Springer Nature},
month = {jul},
url = {https://doi.org/10.1186/s40467-017-0061-8},
number = {1},
pages = {7},
doi = {10.1186/s40467-017-0061-8}
}