Predicting TEC in China based on the neural networks optimized by genetic algorithm

Song, Rui; Zhang, Xuemin; Zhou, Chen; Liu, Jing; He, Jianhui

doi:10.1016/j.asr.2018.03.043

Cited by 74 publications

(51 citation statements)

References 15 publications

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“…Figure 7 shows that the median values of hmF2 errors decreased from 15. 23 31.02% and 27.80%, respectively. The performance of the median error was much more remarkable than that of the RMSE.…”

Section: Validation Of Accuracy In the Solar Moderate Yearmentioning

confidence: 94%

“…In order to solve this problem, the ANN used in the study was optimized by the genetic algorithm (GA). GA is a method based on natural selection for solving both constrained and unconstrained optimization problems; it has been widely applied in many fields [10,30,31]. The GA in the study was used to optimize the initial weight of the ANN, the detail steps were described as following steps and illustrated in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Zhao

Hé

et al. 2020

Remote Sensing

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The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly and Weddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data.Remote Sens. 2020, 12, 866 2 of 17 space-borne and ground-based instruments and data processed techniques, more attention has been paid to the development and improvement of these ionospheric models [3].In the previous studies, the artificial neural network (ANN), nonlinear least squares and AdaBoost techniques have been used to predict ionospheric variations with a satisfactory accuracy [4][5][6][7]. Among them, the ANN technique has proven to be a successful tool in the modeling of ionospheric variations as well as solving the forecast problems in many geophysical applications over a single station, regional area and global scale [3,[8][9][10]. The neural network was first applied to build the foF2 model over the Grahamstown station (33 • S, 26 • E) using the data range set of 1973-1983 [11]. This model can predict the daily noon foF2 value with a root-mean-square error (RMSE) of 0.95 MHz and the monthly averaged RMSE value was 0.48 MHz. Afterward, this approach has been widely applied in the Asia/Pacific sector [5], equatorial region [12], European region [13] and the auroral zone [14]. These studies showed an improvement in the accuracy of the ANN-based models over the International Reference Ionosphere (IRI) model.With the advancement of data acquisition, more ionospheric measurements have become available for the global modelling of foF2. For instance, Oyeyemi et al. [15] used the foF2 data from 59 globally distributed ionospheric stations to establish th...

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Section: Validation Of Accuracy In the Solar Moderate Yearmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Zhao

Hé

et al. 2020

Remote Sensing

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“…In [16] and [17] regional neural models based on MLP networks were suggested to predict the TEC values and to perform the calculation of the time delay [16] and the carrier phase advance [17] of the EM signal in the ionosphere above the Mediterranean area. Regional neural model for the prediction of TEC values above the China, realized by using genetic algorithm-based neural network (GA-NN) and measured TEC values from 43 permanent GPS stations in China was shown in [18]. This paper represents the continuation of the research conducted in [16,17].…”

Section: Introductionmentioning

confidence: 92%

“…In addition to that, thanks to its powerful interpolation and generalization capabilities, the neural model provides a better accuracy prediction of TEC value in comparison with the classical statistical models in geographic areas with a sparse distribution of probe stations in the ionosphere. The mentioned characteristics of ANNs for the modelling of the ionosphere and prediction of TEC values were demonstrated in [12][13][14][15][16][17][18]. In [12] a regional TEC model based on ANN has been designed and tested using data sets collected by the Brazilian GPS network covering periods of low and high solar activity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the EM signal delay in the ionosphere using neural model

Stankovic

Dončov

2019

FACTA U EE

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Neural model capable to accurately and efficiently predict a propagation delay of electromagnetic signal in the ionosphere is proposed in this paper. The model performs this prediction for a given geographic location in Europe between 40 (N) and 70 (N) latitude and 10 (W) and 30 (E) longitude, according to the following parameters: particular day in a year, time during the day and frequency of a signal carrier. Architecture of the model consists of four multilayer perceptron (MLP) networks with the task to estimate, for the known values of the previously mentioned input parameters of the model, the approximate value of free ions concentration in the atmosphere along the signal propagation path above the geographic location of the receiver. Based on the estimated ions concentration and taking into account the considered frequency of the signal carrier, the model calculates the time delay of signal propagation in the ionosphere. The developed neural model is applicable on the whole territory of Republic of Serbia, for all four weather seasons in the period of low solar activity. The results of using the proposed model for the prediction of time delay of the GPS (Global Positioning System) signal in the area of city of Niš are provided in the paper.

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“…In [40], a k-means was optimized through a GA, which esteems the impact of isolated points. Several studies also suggested a number of approaches for NN optimization by GA [41][42][43][44].…”

Section: Literature Review On Multi-modal Emotion Recognitionmentioning

confidence: 99%

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

2019

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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.

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Predicting TEC in China based on the neural networks optimized by genetic algorithm

Cited by 74 publications

References 15 publications

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Prediction of the EM signal delay in the ionosphere using neural model

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

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