Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity

Materials and Design

Volumn 31, Issue 3, 2010, Pages 1042-1049

  Yang, Z ^a   Gu, X S ^b   Liang, X Y ^a   Ling, L C ^a  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Carbon fiber composite; Genetic algorithms; Integrated conductivity; Least squares support vector regression; Modeling

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; CARBON FIBER COMPOSITE; EXPERIMENTAL DATA; GENERALIZATION PERFORMANCE; LEAST SQUARES SUPPORT VECTOR REGRESSION; MATERIAL DESIGNS; MODELING METHOD; NEW TECHNOLOGIES; PERFORMANCE PARAMETERS; PREDICTING MODELS; RESIN MATRIX COMPOSITES; SMALL SAMPLE SIZE PROBLEMS;

CONDUCTING POLYMERS; EXPERIMENTS; FACE RECOGNITION; GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; RESINS; STRUCTURAL MEMBERS; SUPPORT VECTOR MACHINES; TENSILE STRENGTH; VECTORS;

CARBON FIBERS;

EID: 72049098529     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2009.09.057     Document Type: Article

References (20)

1. Trumpler R.J., and Glatz R.J. Conducting polymer composites. J Electroceram 3 4 (1999) 329-346
2. Yumitori1 S., Wang D., and Jones F.R. The role of sizing resins in carbon fibre-reinforced polyethersulfone (PES). Composites 25 7 (1994) 698-705
3. Marshall P.I., Attwood D., and Healey M.J. Interface chemistry in carbon fibre/epoxy matrix composites. Composites 25 7 (1994) 752-756
4. Yang Z., Gu X.S., and Liang X.Y. A radial basis function neural network-based model to predict property of additives modified carbon material high temperature binder. New Carbon Mater 22 (2007) 349-354
5. Marmey P., Porté M.C., and Baquey C. PVDF multifilament yarns grafted with polystyrene induced by γ-irradiation: influence of the grafting parameters on the mechanical properties. Nucl Instrum Methods Phys Res, Sect B 208 (2003) 429-433
6. Haque M.E., and Sudhakar K.V. ANN back propagation prediction model for fracture toughness in microalloy steel. Int J Fatigue 24 (2002) 1003-1017
7. Arumugam M.S., and Rao M.V.C. On the optimal control of single-stage hybrid manufacturing systems via novel and different variants of particle swarm optimization algorithm. Discrete Dyn Nat Soc 19 (2005) 257-279
8. Cortes C., and Vapnik V. Support vector networks. Mach Learn 20 3 (1995) 273-297
9. Tipping M.E. Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1 (2001) 211-244
10. Ao J.Q., Peng Y., Quan H., et al. Support vector machining optimizing compositions of the slag cementing material. J Build Mater 4 (2006) 391-398
11. Üstün B., Melssen W.J., and Buydens L.M.C. Visualisation and interpretation of support vector regression models. Anal Chim Acta 595 (2007) 299-309
12. Nello C., and John S.T. An introduction to support vector machines and other Kernel-based learning methods (2000), Cambridge University Press
13. Suykens J.A.K., and Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett 9 3 (1999) 293-300
14. Kuhn HW, Tucher AW. Nonliner Programming. In: Proceedings of the second Berkeley symposium on mathematical statistics and probabilistics. University of California Press; 1951.
15. Suykens J.A.K., Vandewalle J., and De M.B. Optimal control by least squares support vector machines. Neural Networks 14 (2001) 23-35
16. Keerthi S.S., and Lin C.J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15 3 (2003) 1667-1689
17. Xing W.S., and Xie J.X. Modern heuristic algorithm (1999), Tsinghua University Press, Beijing
18. Davis L. Handbook of genetic algorithms (1991), Van Nostrand Reinhold, New York
19. Math Works Incorporation. MATLAB user manual, version 7.1 R14. Math Works; 2005.
20. Yang Z., Gu X.S., Liang X.Y., et al. Optimization and prediction research on carbon fiber composite integrated conductive performance based on PCA-GANN pattern recognition. Comput Appl Chem 25 12 (2008) 1543-1548