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Proceedings Paper

Holographic neuro-predictor for fractional Brownian motion
Author(s): Alexander V. Pavlov; Ravil Z. Zakirov; Vlad S. Bilyk; Vladimir V. Vedeneev
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Paper Abstract

In this paper we discuss our approach to based on holographic techniques implementation of neuro-fuzzy predictor for processes, described by Fractal Brownian Motion (FBM) model. We use the model of the predictor as a Riemann - Stieltjes integral over the observed traffic of specific weight function. We discuss two-layered bi-directional optical neural network to find our solution. To find the weight function we use non-linearity in the correlation layer of the neural network. In our experiments we used air-photograph of forest as this kind of images demonstrates self-similarity property and can be described by the FBM model. As a first step we used approximate solution for the weight function, achieved by using binary filtering function in the correlation layer. We demonstrate experimental results and discuss directions of our future investigations.

Paper Details

Date Published: 8 July 2003
PDF: 6 pages
Proc. SPIE 5036, Photonics, Devices, and Systems II, (8 July 2003); doi: 10.1117/12.498462
Show Author Affiliations
Alexander V. Pavlov, S.I. Vavilov State Optical Institute (Russia)
St. Petersburg Institute of Fine Mechanics and Optics (Russia)
Ravil Z. Zakirov, S.I. Vavilov State Optical Institute (Russia)
St. Petersburg Institute of Fine Mechanics and Optics (Russia)
Vlad S. Bilyk, St. Petersburg Institute of Fine Mechanics and Optics (Russia)
Vladimir V. Vedeneev, St. Petersburg Institute of Fine Mechanics and Optics (Russia)


Published in SPIE Proceedings Vol. 5036:
Photonics, Devices, and Systems II

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