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During planning and designing of new products the design managers are interested in estimating the cost as early as possible. However, at the early design stage only a few attributes of the future product are known, and their impact on cost is not clear to the cost estimation expert. Neural networks can be used to detect the hidden relationships between cost drivers and the cost of a new product, and estimate the cost after being presented a small set of conceptual attributes describing the product.
Usually, knowledge to be learned by neural networks is represented implicitly in the training samples. The ability to insert knowledge apart from the implicit representations in training samples (“background knowledge”) gives rise to the hope that the learning and operation behavior of neural networks can be improved. In this paper, we develop a method to accomplish the insertion of expert knowledge into the error function during training.
信息时代的制造业及信息的价值 任守
(1995)
