scholarly journals Using RBF neural networks to identify relationship between development of oil prices in world market and value of Chinese currency

2020 ◽  
Vol 73 ◽  
pp. 01008
Author(s):  
Jakub Horák ◽  
Jaromír Vrbka ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Time Lag ◽  
Deep Understanding ◽  
Oil Prices ◽  
World Market ◽  
Rbf Neural Networks ◽  
Related Research ◽  
Research Areas

The objective of the contribution is to identify a possible relationship between the development of the price of Brent oil (Brent in USD/barrel) and the CNY / USD Exchange rate by means of artificial neural networks. Understanding future fluctuation characteristics and the trend in oil prices is the basis for a deep understanding of systemic mechanisms and trends in related research areas. However, given the complexities of oil prices, it is very difficult to obtain accurate forecasts. Within the experiment, a total of 50,000 artificial RBF neural networks were generated. Was found the CNY / USD price will play a significant role in creating China's real product. Given that it was already proven that the CNY / USD exchange depends on Brent in USD / barrel, it is important to focus the further research on finding out the time lag with which the price of Brent in USD / barrel is actually reflected in the price of CNY / USD.

Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

2020 ◽  
Vol 4 (14) ◽  
Author(s):  
Fathi Ahmed Ali Adam, Mahmoud Mohamed Abdel Aziz Gamal El-Di
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Network Models ◽  
Neural Network Models ◽  
Classical Models ◽  
Artificial Neural ◽  
The Exchange Rate ◽  
Long Term Forecasting ◽  
Artificial Neural Network Models

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

2006 ◽  
pp. 28-42
Author(s):  
Ruhul A. Sarker ◽  
Hussein A. Abbass
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Real World ◽  
Network Architectures ◽  
Research Areas ◽  
Artificial Neural ◽  
Multiobjective Algorithms ◽  
Simultaneous Evolution ◽  
Real World Problems

Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.

Prediction of Viscosity of Nanofluids Using Artificial Neural Networks

2014 ◽  
Author(s):  
Ningbo Zhao ◽  
Shuying Li ◽  
Zhitao Wang ◽  
Yunpeng Cao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rbf Neural Network ◽  
Accurate Estimation ◽  
Rbf Neural Networks ◽  
Ann Model ◽  
Model Based ◽  
Viscosity Prediction ◽  
Artificial Neural ◽  
Prediction Approach

The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, a novel viscosity prediction approach using artificial neural networks (ANN) is introduced as an alternative to the model-based viscosity prediction approach to provide a quick and accurate estimation of nanofluids viscosity. Radial basis function (RBF) neural networks has been utilized to form viscosity prediction architectures. Alumina (Al2O3)-water nanofluids from existing literatures were used to test the effectiveness of the proposed method. The results showed that RBF neural network model had a reasonable agreement in predicting experimental data. The findings of this paper indicated that the ANN model was an effective method for prediction of the viscosity of nanofluids and had better prediction accuracy and simplicity compared with the other existing theoretical methods.

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