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      Effectiveness of ECE R66 and FMVSS 220 standards in rollover crashworthiness assessment of paratransit buses

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          Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates

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            Sensitivity measures,anova-like Techniques and the use of bootstrap

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              Approximation of nonlinear systems with radial basis function neural networks.

              A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented. The method uses an n-dimensional raised-cosine type of RBF that is smooth, yet has compact support. The RBF network coefficients are low-order polynomial functions of the input. A simple computational procedure is presented which significantly reduces the network training and evaluation time. Storage space is also reduced by allowing for a nonuniform grid of points about which the RBFs are centered. The network output is shown to be continuous and have a continuous first derivative. When the network is used to approximate a nonlinear dynamic system, the resulting system is bounded-input bounded-output stable. For the special case of a linear system, the RBF network representation is exact on the domain over which it is defined, and it is optimal in terms of the number of distinct storage parameters required. Several examples are presented which illustrate the effectiveness of this technique.
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                Author and article information

                Journal
                International Journal of Automotive Technology
                Int.J Automot. Technol.
                Springer Science and Business Media LLC
                1229-9138
                1976-3832
                June 2014
                May 28 2014
                June 2014
                : 15
                : 4
                : 581-591
                Article
                10.1007/s12239-014-0061-3
                fc21e44c-036f-4df5-847e-88b157d67b9f
                © 2014

                http://www.springer.com/tdm

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