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Integration of rough set and neural network ensemble to predict the configuration performance of a modular product family

Configuration performance prediction (CPP) is critical in the whole process of configuration design for a modular product family. Its aim is to estimate the key performance parameter values in advance, thus evaluating if the product variant can satisfy the customers’ personalised requirements or not. In this paper, we propose a novel prediction approach based on the integration of rough set and neural network ensemble through discovering the knowledge from the historical configuration information table. The minimal hitting set is introduced and its equivalence relationship with the minimal attribute reduction is proven. A genetic algorithm is designed to perform the approximate reduction of the condition attributes. A neural network ensemble model used for regression prediction is constituted by means of the variant bagging method based on error clustering. This methodology can reuse the discovered configuration rules and knowledge efficiently, as well as reduce the effort of experimental measurement to some extent. Finally, the applicability of this prediction method is verified on a newly developed refrigerator family.