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Publikācija: Approximation of Internet Traffic Using Robust Wavelet Neural Networks

Publication Type Scientific article indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Approximation of Internet Traffic Using Robust Wavelet Neural Networks
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Research platform None
Authors Jans Jeļinskis
Gunārs Lauks
Keywords Hurst estimation, Traffic engineering, Wavelet Neural Netoworks
Abstract Robust Hurst parameter estimation of traffic data traces tops the bill of nowadays problems of the field of traffic engineering. Almost every going approach fits up the goal of as far as possible precise H parameter estimation; however this option is not as indispensable as approximate estimation of boundaries of H parameter if traffic demonstrates long range dependence. Constantly this is satisfactory condition for defining adequate traffic engineering operations. In this paper we verify a possibility of robust wavelet based H parameter estimation algorithm with ulterior traffic classification, which is based on wavelet transform of fractional Brownian motion synthesized data traces, and forthcoming wavelet coefficient clustering and operating with neural network learning capabilities. In this paper algorithm is described. Experimental data are depicted and future research subjects are pointed. Ill. 5, bibl. 14
Hyperlink: http://eejournal.ktu.lt/index.php/elt/article/view/11190 
Reference Jeļinskis, J., Lauks, G. Approximation of Internet Traffic Using Robust Wavelet Neural Networks. Electronics and Electrical Engineering, 2008, Vol. 86, No. 6, pp.81-84. ISSN 1392-1215. e-ISSN 2029-5731.
Additional information Citation count:
  • Scopus  0
ID 3093