Neural-Network-Driven fuzzy reasoning for product development processes

Authors:
Yingkui Gu;Hongzhong Huang;Yonghua Li
Affiliations:
Key Lab. for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, Liaoning, China;Key Lab. for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, Liaoning, China;Key Lab. for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

The quantitative and qualitative dependency measures of serial and parallel product development processes are analyzed. And the neural-network-driven fuzzy reasoning mechanism of dependency relationships is developed in the case that there is no sufficient quantitative information or the information is fuzzy and imprecise. In the reasoning mechanism, a three-layer feedforward neural network is used to replace fuzzy evaluation in the fuzzy system. A hybrid learning algorithm that combined unsupervised learning and supervised gradient-descent learning procedures is used to build the fuzzy rules and train membership functions.