Total Vehicle Concept Design using Computational Intelligence

This paper evaluates five methods from the field of computational intelligence and machine learning with regard to their suitability for total vehicle concept design. These methods are used to mimic the input-output relation of a complex model, based on a limited number of expensive simulations (metamodeling). Important steps of this model building process will be investigated and possibilities for model evaluation will be discussed. As the theoretical background is provided, Artificial Neural Network (ANNs), Multivariate Adaptive Regression Splines (MARS), Gaussian Process Models (GPMs), Regression Trees (RTREES) and Support Vector Machines (SVMs) are implemented and compared regarding their accuracy and speed of modeling and computation. A setting from an early stage of total vehicle concept design serves as use case. It can be summarized that the implemented metamodels, except RTREES and SVMs, achieve good approximation accuracy. The ANN proved to be superior method compared to MARS and GPMs. However, the training of the ANN took the most time, which may be of relevance if the modeling process has to be carried out very frequently. Then GPM or MARS offer a good compromise between training duration and accuracy.     «

This paper evaluates five methods from the field of computational intelligence and machine learning with regard to their suitability for total vehicle concept design. These methods are used to mimic the input-output relation of a complex model, based on a limited number of expensive simulations (metamodeling). Important steps of this model building process will be investigated and possibilities for model evaluation will be discussed. As the theoretical background is provided, Artificial...     »