基于汽车故障数据的保修成本预测

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI:10.1109/IJCNN.2007.4370939

T. Hrycej, M. Grabert

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引用次数: 10

摘要

从故障数据库中得到故障和保修成本模型。该模型将统计成分与多层感知器和基于交叉熵的学习规则相结合。该模型用于预测不同保修条件下的保修成本。预测方差的估计既考虑了单个车辆的风险，也考虑了整体的制造质量波动风险。

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Warranty Cost Forecast Based on Car Failure Data

A failure and warranty cost model is gained from a failure database. The model is a combination of statistical components with a multi-layer perceptron and a cross-entropy based learning rule. The model is used for forecasting warranty costs in alternative warranty condition scenarios. The estimate of forecast variance considers both the individual vehicle risk and the overall manufacturing quality fluctuation risk.

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2007 International Joint Conference on Neural Networks

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