scholarly journals Predicting Foreign Exchange Using Digital Signal Processing

2021 ◽  
Vol 4 (2) ◽  
pp. 1-11
Author(s):  
Robinson M. ◽  
Kabari L.G.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Foreign Exchange ◽  
Digital Signal ◽  
Digital Processing ◽  
Research Community ◽  
Market Volatility ◽  
Network Algorithms ◽  
Price Patterns ◽  
Financial Losses

The forex market is one associated with so much volatility and can lead to grave financial losses if not properly understood. To understand the market is to study the price patterns from previous years or months and make predictions from the rate of falling and rising. There have been so much researches aimed at developing a predictive model for the FOREX market, however, no model has been able to handle the market volatility while predicting future rates accurately. In this work, we have developed a digital processing model for predicting foreign exchange using ARIMA and Artificial Neural Network algorithms. We used price datasets for five currencies namely: USD, Swiss Pounds, Yen, Euro and Franc, gotten from the Central Bank of Nigeria (CBN) website. The data ranged from a period of 20 years. The model was simulated using MATLAB software. The study performed excellently in terms of time (26 seconds) and minimal errors (0.7). This work could be beneficial to FOREX traders and to the entire research community.

A study on the estimations of (n, t) reaction cross-sections at 14.5 MeV by using artificial neural network

2021 ◽  
pp. 2150168
Author(s):  
Hasan Özdoğan ◽  
Yiğit Ali Üncü ◽  
Mert Şekerci ◽  
Abdullah Kaplan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cross Sections ◽  
Reaction Cross ◽  
Forward Algorithm ◽  
Network Algorithms ◽  
Analytical Formulas ◽  
Artificial Neural ◽  
Semi Empirical ◽  
Reaction Cross Sections

In this paper, calculations of the [Formula: see text] reaction cross-sections at 14.5 MeV have been presented by utilizing artificial neural network algorithms (ANNs). The systematics are based on the account for the non-equilibrium reaction mechanism and the corresponding analytical formulas of the pre-equilibrium exciton model. Experimental results, obtained from the EXFOR database, have been used to train the ANN with the Levenberg–Marquardt (LM) algorithm which is a feed-forward algorithm and is considered one of the well-known and most effective methods in neural networks. The Regression [Formula: see text] values for the ANN estimation have been determined as 0.9998, 0.9927 and 0.9895 for training, testing and for all process. The [Formula: see text] reaction cross-sections have been reproduced with the TALYS 1.95 and the EMPIRE 3.2 codes. In summary, it has been demonstrated that the ANN algorithms can be used to calculate the [Formula: see text] reaction cross-section with the semi-empirical systematics.

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