Abstract
The assessment on hardware design defects plays an important role in the product development process. In order to improve the design quality through mitigating design defects, a model for assessing hardware product design defects is developed in this paper based on the design defects formation mechanism of product. A novel assessment method by applying fuzzy theory, genetic algorithm and neural network is proposed to construct assessment model. Assessment index is quantified by fuzzy language description. Fuzzy BP neural network tuned by genetic algorithm with the purpose of optimizing the connection weights and avoiding local minimum is used to evaluate probability of product design defects occurrence. The results obtained in this study demonstrated that the assessment model had excellent capabilities with a high accuracy and good training speed, thus provides an effective tool for assessing design defects of hardware product.
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Acknowledgments
The authors gratefully acknowledge the enterprises that provide data and design experts who participate in works of data fuzzy processing. We also thank M.S. Li and Yang Liu for their suggestions and providing language help on the earlier version of this article. This research is supported by the National Natural Science Foundation of China (Grant No. 71161018).
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Zheng, H., Liu, W., Xiao, C., Hu, W. (2014). Assessment of Hardware Product Design Defects by Fuzzy Neural Network Based on Genetic Algorithm. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55182-6_3
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DOI: https://doi.org/10.1007/978-3-642-55182-6_3
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