A New Intelligent Estimation Model of Material Fatigue Limit

Yi Wan

doi:10.4028/www.scientific.net/AMR.108-111.1235

Paper Titles

Maximal Frequent Item Sequences Mining
p.1211

Wireless Temperature Sensor Network for Kiln in Ceramic Industry
p.1217

Spatial-Temporal Non-Uniform Subband Broadband Beamforming
p.1223

Several Properties of Quadratic Surface Tangent to Plane
p.1229

A New Intelligent Estimation Model of Material Fatigue Limit
p.1235

Study on Grey Forecast Method of Chaotic Time Series
p.1239

Study on Peanut Leaves’ Delayed Luminescence Characteristic under Excitation of Alternating Electric Field
p.1244

The Construction of Tumor Cell Apoptosis Signal Transduction Network and the Analysis of its Regulation Mechanism
p.1250

Research on Compression Methods for ASCII STL File
p.1254

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 108-111A New Intelligent Estimation Model of Material...

A New Intelligent Estimation Model of Material Fatigue Limit

Abstract:

A fuzzy support vector machine kernel regression method of fatigue limit calculating is presented aiming at difficulty of material fatigue limit calculating and disadvantage of the other forecast model. Complicated and strong nonlinear fatigue limit of material is simulated by parameter optimized design and conformation of fuzzy support vector combinative algorithm, fuzzy support vector optimized algorithm has strong ability of nonlinear mapping and functional approach, it avoids availably phenomenon of partial minimum and overfitting, and gains high precision by comparing numerical value of network output with numerical value of experiment.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 108-111)

Pages:

1235-1238

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.108-111.1235

Citation:

Cite this paper

Online since:

May 2010

Authors:

Yi Wan

Keywords:

Fuzzy Support Vector Machine, Kernel Regression Method, Material Fatigue Limit, Parameter Optimized Design

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] G. Minak: Journal of Testing and Evaluation Vol. 2(2007), pp.1-8.

Google Scholar

[2] J. R. Kenneth: Journal of Computational & Graphical Statistics Vol. 9(2003), pp.2-10.

Google Scholar

[3] A. Pedro and M. Ian: Journal of the Structural Division Vol. 12(1989), pp.2657-2675.

Google Scholar

[4] Y. Wan: Journal of Information & Computational Science Vol. 5 (2008), pp.1085-1091.

Google Scholar

[5] Y. Wan: Journal of Information & Computational Science Vol. 5 (2008), pp.1813-1818.

Google Scholar

[6] Y. Wan: IEEE computer society, Fourth International Conference on Natural Computation Vol. 3 (2008), pp.175-179.

Google Scholar

[7] G. Jiang, J. Xiao: Control and Decision Vol. 9(2006), pp.1054-1058.

Google Scholar

[8] C. Nello, S. T. John: (University Press, Cambridge 2000).

Google Scholar

[9] V. N. Vapnik: Statistical Learning Theory (Springer-Verlag, New York 2000).

Google Scholar

[10] C. W. Hsu, C. J. Lin: IEEE Trans on Neural Networks Vol. 13 (2002), pp.415-425.

Google Scholar

[11] X. Y. Zhao, D. W. Wang: Control Theory and Applications Vol. 19(2002), pp.249-253.

Google Scholar

[12] S.D. Ding: Machine intensity Vol. 19(l997), pp.45-47.

Google Scholar

[13] S. B. Zhao, Z. B. Wang: Fatigue design (Machinery Industry Press, Beijing 2002). Fig. 5 The relation between copper material fatigue limit and stress ration.

Google Scholar