Anchor Selection In Angle-Of-Arrival Estimation-Based Localization Using Polynomial Chaos Expansions

Vorst, Thomas Van der; Nguyen, Trung-Hien; Monfared, Shaghayegh; Benlarbi-Delaï, Aziz; Sarrazin, Julien; Horlin, Francaois; Doncker, Philippe De

doi:10.1109/vtc2020-spring48590.2020.9129420

Cited by 1 publication

(1 citation statement)

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“…The BPNN tuned SVM grey model in [55] is used for forecasting the signal deviation time series. The anchor selection method is based on polynomial chaos expansions [86] for angle-of-arrival prediction-based positioning systems. The chaos algorithm in [92] is proposed for forecasting the radio wave propagation in the ionosphere.…”

Section: Communication Signals and Systemsmentioning

confidence: 99%

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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Section: Communication Signals and Systemsmentioning

confidence: 99%