The neural network technique––1: a general exposition

Tulunay, Yurdanur; Tulunay, E.; Şenalp, Erdem Türker

doi:10.1016/j.asr.2003.06.008

Cited by 33 publications

(24 citation statements)

References 8 publications

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“…A neuron is an information processing unit which consists of a connecting link, adder and activation function. The neuron patterns are similar to biological neural nets and are modeled after the human brain (Tulunay et al, 2004a). NN is an important tool for nonlinear approximation when it is trained with sufficient historic data (Habarulema et al, 2007).…”

Section: Neural Networkmentioning

confidence: 99%

TEC prediction with neural network for equatorial latitude station in Thailand

et al. 2012

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This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station.

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Section: Neural Networkmentioning

confidence: 99%

TEC prediction with neural network for equatorial latitude station in Thailand

et al. 2012

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“…Tulunay et al, 2000Y. Tulunay et al, , 2001Y. Tulunay et al, , 2004aRadicella and Tulunay, 2004;Stamper et al, 2004;Tulunay et al, 2006).…”

Section: Metu Fuzzy Neural Network Model (Metu-fnn-m)mentioning

confidence: 99%

“…To the best knowledge of the authors, it is the first time that a Fuzzy Neural Network with Bezier surface mapping technique is developed in order to forecast and map the parameters of such Near Earth space processes. The forecast modeling process consists of (i) "training"; (ii) "validation within training; and (iii) "validation within operation" phases (Tulunay et al, 2004a). Unless otherwise stated the "validation within operation" will be referred as "operation".…”

Section: Metu Fuzzy Neural Network Model (Metu-fnn-m)mentioning

confidence: 99%

“…Highly nonlinear processes in the Near Earth Space are advantageously dealt with using data driven modeling techniques in the Neural Network (NN) approach providing there are representative data (e.g. Tulunay et al, 2004a). Therefore, there is a clear need for observational studies to assess the evidence for the operation of the mechanisms proposed at climate space and time scales (Todd and Kniveton, 2001;Usoskin et al, 2004).…”

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confidence: 99%

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A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Tulunay¹,

Şenalp²,

Öz³

et al. 2008

Ann. Geophys.

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Abstract. Atmospheric processes are highly nonlinear. A small group at the METU in Ankara has been working on a fuzzy data driven generic model of nonlinear processes. The model developed is called the Middle East Technical University Fuzzy Neural Network Model (METU-FNN-M). The METU-FNN-M consists of a Fuzzy Inference System (METU-FIS), a data driven Neural Network module (METU-FNN) of one hidden layer and several neurons, and a mapping module, which employs the Bezier Surface Mapping technique. In this paper, the percent cloud coverage (%CC) and cloud top temperatures (CTT) are forecast one month ahead of time at 96 grid locations. The probable influence of cosmic rays and sunspot numbers on cloudiness is considered by using the METU-FNN-M.

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“…Focusing in the prediction of the foF2, significant contribution for operational applications comes from data-driven empirical and semi-empirical models that exploit either time series forecasting techniques such as the standard autocorrelation as well as auto-covariance and neural networks (e.g. Koutroumbas et al 2008;Koutroumbas & Belehaki 2005;Tulunay et al 2004aTulunay et al , 2004bCander 2003;Stanislawska & Zbyszynski 2001McKinnell & Poole 2001;Wintoft & Cander 2000a, 2000b or space weather indices and proxies as drivers of the ionospheric response during disturbed conditions (e.g. Mikhailov et al 2007;Muhtarov et al 2002;Kutiev & Muhtarov 2001, 2003Muhtarov & Kutiev 1999;Pietrella & Perrone 2008;Tsagouri et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the performance of DIAS ionospheric forecasting models

Tsagouri

2011

J. Space Weather Space Clim.

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Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr). Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i) the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF), which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii) the Geomagnetically Correlated Autoregression Model (GCAM), which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models' predictions with actual observations obtained over almost one solar cycle (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome) and on the comparison of the models' performance against two simple prediction strategies, the median-and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.

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The neural network technique––1: a general exposition

Cited by 33 publications

References 8 publications

TEC prediction with neural network for equatorial latitude station in Thailand

TEC prediction with neural network for equatorial latitude station in Thailand

A fuzzy neural network model to forecast the percent cloud coverage and cloud top temperature maps

Evaluation of the performance of DIAS ionospheric forecasting models

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