Prediction Simulation for the Length of Garment Marking Based on QPSO-DFNN

Qisheng Yan*, 1, 2, Rong Zheng2
1 School of Digital Media, Jiangnan University, Wuxi 214122,China
2 School of Science, East China Institute of Technology, Nanchang 330013, China

© 2014 Yan and Zheng

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Because of the defects of a posteriori and experience-depended traditional method, a prediction model of the length of garment marking based on dynamic fuzzy neural network (DFNN) combined with Quantum-behaved Particle Swarm Optimization (QPSO) was proposed. The salient characteristics of the method are: 1) hierarchical on-line selforganizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system’s performance; and 3) fast learning speed can be achieved. Data obtained from 32 and 10 groups are used for the dynamic fuzzy neural network learning and simulation respectively. The simulation results demonstrate that DFNN can be used as a prediction system for the length of garment marking and absolute value of the maximum relative error less than 5.11%, and mean absolute percentage error less than 2.01%. The DFNN may be the construction for factory to estimate the fabric consumption and provide a new method for designing optimal cutting plan.

Keywords: BP neural network, dynamic fuzzy neural networks (DFNN), marking lengths, prediction, QPSO.