Frequency Hopping Prediction Based on Multi-Kernel SVM

Yu Kai Yao; Yong Qing Yu; Yang Liu; Jin Jin Wang; Xiao Yun Chen

doi:10.4028/www.scientific.net/AMM.336-338.2256

Paper Titles

Two Partitioning Algorithms for Generating of M Sets of the Frieze Group
p.2238

Embedding Two Edge-Disjoint Hamiltonian Cycles into Parity Cubes
p.2248

Study of an Improved Petri Net Model for Scheduling of DEDS
p.2252

Frequency Hopping Prediction Based on Multi-Kernel SVM
p.2256

Calculation of Cosmic Radiation Exposure on Flights with Computer Program
p.2261

Solutions for a Class of the Higher Diophantine Equation
p.2265

A Hybrid Document Recommender Algorithm Based on Random Walk
p.2270

Study on Flexible Dynamic Visualization Techniques
p.2277

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 336-338Frequency Hopping Prediction Based on Multi-Kernel...

Frequency Hopping Prediction Based on Multi-Kernel SVM

Abstract:

The chaotic frequency hopping sequences possesses short-term predictability. Via the phase space reconstruction approach, we can get chaos attractors, and the problem of series prediction can be transformed into the regression problem of the chaotic attractors. This paper uses SVR method to deal with the prediction of frequency hopping sequences. After analyzing the characteristics of existing kernel functions, we produce a new multi-kernel function, which is used for the prediction of frequency hopping sequences. Experiments show the fine performances of our methods.

You might also be interested in these eBooks

Industrial Instrumentation and Control Systems II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 336-338)

Pages:

2256-2260

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.336-338.2256

Citation:

Cite this paper

Online since:

July 2013

Authors:

Yu Kai Yao, Yong Qing Yu, Yang Liu, Jin Jin Wang, Xiao Yun Chen

Keywords:

Frequency Hopping, Multi-Kernel Function, Prediction, Support Vector Regression (SVR)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.D. Farmer, J.L. Sidorowich: Physical Review Letters, 59, 8, pp.845-848. (1997).

Google Scholar

[2] M. Han in: Prediction Theory and Method of Chaotic Time Series, edited by M. Han, China Water Power Press (2007).

Google Scholar

[3] L. P. Maguire, B. Roche, T. M. Mcginnity: Information Sciences, 112(1998), pp.125-136.

Google Scholar

[4] W. Heng, Y. L. Yang, Y. Y. Zhu: Application Research of Computers, 29(1), pp.237-240. (2012).

Google Scholar

[5] J. L. Dang, J. S. Zhang: Signal Processing, 21, 4A, pp.122-125. (2005).

Google Scholar

[6] S. F. Taken: Lecture Notes in Mathematics, 898(1981), pp.366-381.

Google Scholar

[7] H. Kantz, T. Schreiber in: Nonlinear Time Series Analysis, edited by H. Kantz, T. Schreiber, Cambridge: Cambridge University Press (1997).

Google Scholar

[8] A. M. Fraser, H. L. Swinney: Phys. Rev. A, 33(1986), pp.1134-1140.

Google Scholar

[9] Z. X. Zhou，Y. N. Gao，J. B. Chen: Journal of system simulation, 18, 1, pp.173-175. (2006).

Google Scholar

[10] Z. B. Lu, Z. M. Cai, K. Y. Jiang: Journal of system simulation，19, 11, pp.2527-2530. (2007).

Google Scholar

[11] V. Vapnik. New York: Springer Verlag, (1999).

Google Scholar

[12] G. F. Smits, E. M. Jordan: IEEE Proceeding of IJCNN '02 on Neural Networks, 3(2002), pp.2785-2790.

Google Scholar

[13] Z. M. Yang，G. L. Liu: The Uncertain Support Vector Machine: Algorithms and Applications, edited by F. J. Liu, Science Press Ltd (2012).

Google Scholar