Appraisal of ANN and ANFIS for Predicting Vertical Total Electron Content (VTEC) in the Ionosphere for GPS Observations

Positional accuracy in the usage of GPS receiver is one of the major challenges in GPS observations. The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the transmission of signals to the Earth. Therefore, the total electron content is a key parameter in mitigating ionospheric effects on GPS receivers. Many researchers have therefore proposed various models and methods for predicting the total electron content along the signal path. This paper focuses on the use of two different models for predicting the Vertical Total Electron Content (VTEC). Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere. The developed ANN and ANFIS model gave Root Mean Square Error (RMSE) of 1.953 and 1.190 respectively. From the results it can be stated that the ANFIS is more suitable tool for the prediction of VTEC. Keywords: Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Vertical Total Electron

Download Full-text

Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting

Neural Computing and Applications ◽

10.1007/s00521-019-04528-8 ◽

2019 ◽

Vol 31 (12) ◽

pp. 8411-8422 ◽

Cited By ~ 3

Author(s):

Dinibel Pérez Bello ◽

María P. Natali ◽

Amalia Meza

Keyword(s):

Neural Network ◽

Total Electron Content ◽

Recurrent Neural Network ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Electron Content ◽

Vertical Total Electron Content ◽

Inference System ◽

Total Electron ◽

Neuro Fuzzy

Download Full-text

Flood hydrograph modeling using artificial neural network and adaptive neuro-fuzzy inference system based on rainfall components

Arabian Journal of Geosciences ◽

10.1007/s12517-021-06683-6 ◽

2021 ◽

Vol 14 (5) ◽

Author(s):

Saeid Janizadeh ◽

Mehdi Vafakhah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy ◽

Flood Hydrograph ◽

Artificial Neural

Download Full-text

Comparison of different modeling methods toward predictive capability evaluation of the bonding strength of wood laminated products

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/09544089211053074 ◽

2021 ◽

pp. 095440892110530

Author(s):

Morteza Nazerian ◽

Seyed Ali Razavi ◽

Ali Partovinia ◽

Elham Vatankhah ◽

Zahra Razmpour

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Bonding Strength ◽

Fuzzy Inference ◽

Molar Ratio ◽

Inference System ◽

Neuro Fuzzy ◽

Artificial Neural

The main aim of this study is usability evaluation of different approaches, including response surface methodoloy, adaptive neuro-fuzzy inference system, and artificial neural network models to predict and evaluate the bonding strength of glued laminated timber (glulam) manufactured using walnut wood layers and a natural adhesive (oxidized starch adhesive), with respect to this fact that using the modified starch can decrease the formaldehyde emission. In this survey, four variables taken as the input data include the molar ratio of formaldehyde to urea (1.12–1.52), nanocellulose content (0%–4%, based on the dry weight of the adhesive), the mass ratio of the oxidized starch adhesive to the urea formaldehyde resin (30:70–70:30), and the press time (4–8 min). In order to find the best predictive performance of each selected evaluation approach, different membership functions were used. The optimal results were obtained when the molar ratio, nanocellulose content, mass ratio of the oxidised starch, and press time were set at 1.22, 3%, 70:30, and 7 min, respectively. Based on the performance criteria including root mean square error (RMSE) and mean absolute percentage error (MAPE) obtained from the modeling of response surface methodology, adaptive neuro-fuzzy inference network, and artificial neural network, it became evident that response surface methodology could offer a better prediction of the response with the lowest level of errors. Beside, artificial neural network and adaptive neuro-fuzzy inference system support the response surface methodology approach to evaluate bonding strength response with high precision as well as to determine the optimal point in fabrication of laminated products.

Download Full-text