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

2019 ◽  
Vol 31 (12) ◽  
pp. 8411-8422 ◽  
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

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

2017 ◽  
Vol 17 (2) ◽  
pp. 12-16
Author(s):  
I. Yakubu ◽  
Y. Y. Ziggah ◽  
D. Asafo-Agyei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Total Electron Content ◽  
Fuzzy Inference ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Inference System ◽  
Total Electron ◽  
Neuro Fuzzy ◽  
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

Model Identification of 3R Palnar Robot using Neural Network and Adaptive Neuro-Fuzzy Inference System

2021 ◽  
Author(s):  
R. Subasri ◽  
R. Meenakumari ◽  
R. Velnath ◽  
Srinivethaa Pongiannan ◽  
M. Sri Sai Mani Rohit Kumar
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Model Identification ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1958 ◽  
Author(s):  
Lilin Cheng ◽  
Haixiang Zang ◽  
Tao Ding ◽  
Rong Sun ◽  
Miaomiao Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Recurrent Neural Network ◽  
Fuzzy Inference ◽  
Ensemble Model ◽  
Probabilistic Forecasting ◽  
Inference System ◽  
Wind Speed Forecasting ◽  
Wind Power Integration ◽  
Neuro Fuzzy

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as submodels for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deeplearningbased submodels. Lastly, variances are obtained from submodels and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

2009 ◽  
Vol 9 (2) ◽  
pp. 746-755 ◽  
Author(s):  
Mohammad Zounemat-Kermani ◽  
Ali-Asghar Beheshti ◽  
Behzad Ataie-Ashtiani ◽  
Saeed-Reza Sabbagh-Yazdi
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Scour Depth ◽  
Pile Groups ◽  
Inference System ◽  
Neuro Fuzzy
Sign in / Sign up

Export Citation Format

Share Document