SEARCH

SEARCH BY CITATION

References

  • [1]
    Joint Research Centre (JRC) of the European Commission: http://sensitivity-analysis.jrc.ec.europa.eu/ (2012).
  • [2]
    A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola, Global Sensitivity Analysis – The Primer (John Wiley and Sons, 2008).
  • [3]
    I. M. Sobol, Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates, Mathematics and Computers in Simulation 55, pp. 271–280 (2001).
  • [4]
    F. Campolongo, J. Cariboni, and A. Saltelli, An effective screening design for sensitivity analysis of large models, Environmental Modelling and Software 22(10), pp. 1509–1518 (2007).
  • [5]
    I. M. Sobol and S. Kucherenko, Derivative based global sensitivity measures, Procedia – Social and Behavioral Sciences 2(6), pp. 7745–7746 (2010).
  • [6]
    G. D. Garson, Interpreting neural-network connection weights, AI Expert, pp. 47–51 (1991).
  • [7]
    T. Tchaban, M. J. Taylor, and J. P. Griffin, Establishing Impacts of the Inputs in a Feedforward Neural Network, Neural Computing and Applications 178, pp. 309–317 (1998).
  • [8]
    M. Gevreya, I. Dimopoulosb, and S. Leka, Two-way interaction of input variables in the sensitivity analysis of neural network models, Ecological Modelling 195, pp. 43–50 (2006).
  • [9]
    S. Pannier and W. Graf, Sectional sensitivity measures with artificial neural networks, Proceedings 9th LS-DYNA User Forum, Bamberg, Germany (Dynamore, 2010).
  • [10]
    J. J. Montano and A. Palmer, Numeric sensitivity analysis applied to feedforward neural networks, Neural Computing and Applications 12, pp. 119–125 (2003).
  • [11]
    J. D. Olden, M. K. Joy, and R. G. Death, An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data, Ecological Modelling 178(3–4), pp. 389–397 (2004).
  • [12]
    U. Reuter, Z. Mehmood, and C. Gebhardt, Efficient classification based methods for global sensitivity analysis, Computers and Structures (submitted 2012).