Research Article | OPEN ACCESS
A Conflict Detection Model Based on Constraint Satisfaction in Food Product Collaborative Design
Kangkang Yang, Shijing Wu and Lu Zhou
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Advance Journal of Food Science and Technology 2016 1:37-42
Received: April 19, 2015 | Accepted: May 10, 2015 | Published: January 05, 2016
Abstract
With the market competition increasing, in order to shorten product development cycle and reduce the development costs, product design is changed from the traditional serial-type process to the parallel, collaborative development process. Food product collaborative design of Feature modeling refers to the number of the design team through the division of labor and cooperation has completed a product development project process. The set with known constraints was detected by interval propagation algorithm. Meanwhile, BP neural network was proposed in this study to detect the set with unknown constraints. Simulated results indicated that BP neural network optimized by IA has better performance in convergent speed and global searching ability compared with Genetic Algorithm (GA).The constraints of two sets were detected respectively.
Keywords:
BP neural network, conflict detection, food product collaborative design,
References
-
Avila, J.D.J.R., A.F. Ramírez and C. Avilés-Cruz, 2008. Nonlinear system identification with a feed forward neural network and an optimal bounded ellipsoid algorithm. WSEAS T. Comput., 7(5): 542-551.
-
FTRD, 2005. Center, Artificial Neural Network Theory and MATLAB Application. Tsinghua University Press, Beijing.
-
Gopalakrishnan, H. and D. Kosanovic, 2015. Operational planning of combined heat and power plants through genetic algorithms for mixed 0-1 nonlinear programming. Comput. Oper. Res., 56: 51-67.
CrossRef -
Haw, S.C. and C.S. Lee, 2009. Extending path summary and region encoding for efficient structural query processing in native XML databases. J. Syst. Software, 82(6): 1025-1035.
CrossRef
-
Hsu, K., H.V. Gupta and S. Sorooshian, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 31(10): 2517-2530.
CrossRef -
Hu, X.P., J. Wang and Z.H. Xu, 2009. Constraint network modeling and conflict detection in vertical collaboration design. Mech. Electr. Eng. Mag., 26(5): 44-47.
-
Huang, B., S. Yi and W.T. Chan, 2004. Spatio-temporal information integration in XML. Future Gener. Comp. Sy., 20(7): 1157-1170.
CrossRef
-
Jaulin, L., 2000. Interval constraint propagation with application to bounded-error estimation. Automatica, 36(10): 1547-1552.
CrossRef
-
Klein, M., 1991. Supporting conflict resolution in cooperative design systems. IEEE T. Syst. Man Cyb., 21(6): 1379-1390.
CrossRef
-
Liu, Y., D. Yang, S. Tang, T. Wang and J. Gao, 2005. Validating key constraints over XML document using XPath and structure checking. Future Gener. Comp. Sy., 21(4): 583-595.
CrossRef -
Meng, X.L., H. Yi, Z.H. Ni and Y. Liu, 2004. Research on technologies of constraint-based conflict detection in collaborative design. Comput. Integr. Manuf., 10(11): 1426-1432.
-
Pierre, A.Y., 2009. A CSP approach for the network of product lifecycle constraints consistency in a food product collaborative design context. Eng. Appl. Artif. Intel., 22(6): 961-970.
CrossRef -
Slimani, K., C.F. Da Silva, L. Medini and P. Ghodous, 2006. Conflict mitigation in food product collaborative design. Int. J. Prod. Res., 44(9): 1681-1702.
CrossRef
-
Wang, D.Y. and W.D. Jin, 2007. Conflict detection algorithm in food product collaborative design. Comput. Appl., 27(3): 650-652.
-
Xie, H.C., D.R. Chen and X.M. Kong, 2002. Constraint-based conflict detection in food product collaborative design. China Mech. Eng., 13(18): 1590-1592.
-
Xiong, Y., W.H. Fan and G.L. Xiong, 2009. Research and realization of distributed conflict detection system. Comput. Eng., 35(20): 23-27.
-
Xu, D., F.F. Yap, X. Han and G.L. Wen, 2003. Identification of spring-force factors of suspension systems using progressive neural network on a validated computer model. Inverse Probl. Eng., 11(1): 55-74.
CrossRef -
Yuan, D.B., X. Li and N.S. Zhu, 2014. Penetration depth forecast using BP neural network-based system. J. Comput. Inform. Syst., 10(12): 5001-5008.
-
Zhang, Y.F., Z.Y. Xiong, Y. Chen et al., 2008. Immune algorithms based on data processing in intrusion detection. J. Comput. Inform. Syst., 4(1): 293-300.
-
Zhao, H.S., L. Tian and B.S. Tong, 2002. Constraint-based conflict detection and negotiation in food product collaborative design. Comput. Integr. Manuf., 8(11): 896-900.
-
Zhu, J. and X.D. Song, 2012. A conflict detection algorithm based on the design history in collaborative CAD design. Comput. Digit. Eng., 5(40): 79-81.
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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