Application of a neural network for detection at strong nonlinear intersymbol interference

Obernosterer, F.; Oehme, Wolfgang; Sutor, Alexander

doi:10.1109/20.617733

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In this paper, a global optimization of neural network [10] is developed to search for the nonlinear global optimization solution of the maximum likelihood. And then, we study the performance of NN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Oil-Spills Detection in Net-Sar Radar Images Using Support Vector Machine

Zhang¹,

Guo²,

Zeng³

et al. 2015

TOAUTOCJ

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During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.

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Section: Introductionmentioning

confidence: 99%

Oil-Spills Detection in Net-Sar Radar Images Using Support Vector Machine

Zhang¹,

Guo²,

Zeng³

et al. 2015

TOAUTOCJ

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“…When materials are involved, which incorporate memory effects, like ferromagnetic, magnetostrictive or piezoelectric materials, the need for models arises, which can reflect this memory. [3][4][5][6][7][8][9] For the modeling of such materials, mathematical descriptions like the Preisach operator have been proposed. [10][11][12] Since the native Preisach operator is static and does not incorporate time or temperature dependent effects, some research on the dynamic extension of the Preisach-Neel model for Stoner-Wohlfarth particle systems has been conducted.…”

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confidence: 99%

A modified Preisach hysteresis operator for the modeling of temperature dependent magnetic material behavior

Sutor¹,

Rupitsch²,

Bi³

et al. 2011

Journal of Applied Physics

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In this paper, we present a model for temperature dependent hysteretic nonlinearities with nonlocal memories. This model can be applied to describe hysteretic material behavior. Common applications are ferromagnetic or magnetostrictive materials. Our model consists mainly of a Preisach operator with a continuous Preisach weight function. We choose a weight function which shows a strong correlation between the function's parameters and certain properties of the hysteresis curve. As a new approach, the weight function is written as a function of temperature. The model parameters are customized to a set of symmetric hysteresis curves. We verify our model for magnetic materials with differently shaped hysteresis curves, different temperatures and magnetic field amplitudes.

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