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Journal of Uncertainty Analysis and Applications

, 5:7

First Online: 11 July 2017Received: 10 January 2017Accepted: 28 June 2017

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.

KeywordsSugeno measure space Dependent-chance programming Sugeno simulation Hybrid approach  Download fulltext PDF



Author: Hong Zhang - Jianwei Song

Source: https://link.springer.com/



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