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Modified GMDH-NN algorithm and its application for global sensitivity analysis
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DOI：10.1016/j.jcp.2017.07.027.png)
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Abstract
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Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. (C) 2017 Elsevier Inc. All rights reserved.
Keywords:
Global sensitivity analysis
High dimensional model representation (HDMR)
Group method of data handling (GMDH) algorithm
Neural network (NN)
Sobol' sensitivity indices
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