Design optimization has been performed for the suspension system of high speed train. Neural network and design of experiment (DOE) have been employed to build a meta-model for the system with 29 design variables and 46 responses. A combination of fractional factorial design and D-optimality design was used as an approach to DOE in order to reduce the number of experiments to a more practical level. As a result, only 66 experiments were enough. The 46 responses were divided into four performance index groups such as ride comfort, derailment quotient, unloading ratio and stability index. Four meta-models for each index group were constructed by use of neural network. For the learned meta-models, multi-criteria optimization was achieved by differential evolution. The results show that the proposed methodology yields a highly improved design in the ride comfort, unloading ratio and stability index.


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    Title :

    Design optimization for suspension system of high speed train using neural network


    Additional title:

    Optimierung der Radaufhängung eines Hochgeschwindigkeitszugs mit Hilfe eines neuronalen Netzwerks


    Contributors:
    Kim, Y.G. (author) / Park, C.K. (author) / Hwang, H.S. (author) / Park, T.W. (author)


    Publication date :

    2003


    Size :

    9 Seiten, 13 Bilder, 3 Tabellen, 15 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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