scholarly journals Temporal solar irradiance variability analysis using neural networks

2015 ◽  
Vol 11 (S320) ◽  
pp. 333-338
Author(s):  
Ambelu Tebabal ◽  
Baylie Damtie ◽  
Melessew Nigussie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Solar Irradiance ◽  
Dominant Role ◽  
Back Propagation ◽  
Total Solar Irradiance ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Surface Magnetism ◽  
Feed Forward

AbstractA feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

scholarly journals Rainfall Prediction Using Artificial Neural Network (ANN) for tarai Region of Uttarakhand

2019 ◽  
pp. 1-7 ◽  
Author(s):  
Pooja Yadav ◽  
Atish Sagar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Rainfall Prediction ◽  
Feed Forward Back Propagation ◽  
Propagation Algorithm ◽  
Term Prediction ◽  
Artificial Neural

Rainfall prediction is clearly of great importance for any country. One would like to make long term prediction, i.e. predict total monsoon rainfall a few weeks or months and in advance short term prediction, i.e. predict rainfall over different locations a few days in advance [1]. Predicted by using its correlation with observed parameter. Several regression and neural network based models are currently available. While Artificial Neural Network provide a great deal of promise, they also embody much uncertainty [2,3]. In this paper, different artificial neural network models have been created for the rainfall prediction of Uttarakhand region in India. These ANN models were created using training algorithms namely, feed-forward back propagation algorithm [4,5]. The number of neurons for all the models was kept at 10. The mean squared error was measured for each model and the best accuracy was obtained by the feed-forward back propagation algorithm with MSE value as low as 0.00547823.

scholarly journals Neural networks used for speech recognition

2010 ◽  
Vol 20 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Wouter Gevaert ◽  
Georgi Tsenov ◽  
Valeri Mladenov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Speech Recognition ◽  
Radial Basis Functions ◽  
Back Propagation ◽  
Classification Performance ◽  
Basis Functions ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Propagation Algorithm

In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neural Networks.

scholarly journals Parallelizing Feed-Forward Artificial Neural Networks on Transputers

1991 ◽  
Vol 20 (369) ◽  
Author(s):  
Svend Jules Fjerdingstad ◽  
Carsten Nørskov Greve
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Neural Net ◽  
Training Phase ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Propagation Algorithm ◽  
Artificial Neural

<p>This thesis is about parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a number of parallelizations of the widely used neural net learning algorithm called <em>back-propagation</em>.</p><p> </p><p>We describe two different strategies for parallelizing the back-propagation algorithm. A number of parallelizations employing these strategies have been implemented on a system of 48 transputers, permitting us to evaluate and analyze their performances based on the results of actual runs.</p>

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

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