Current Status of Artificial Neural Network Applications to Power Systems in the United States

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

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Artificial Neural Networks

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch010 ◽

2011 ◽

pp. 222-243

Author(s):

Joarder Kamruzzaman ◽

Ruhul Sarker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Network Models ◽

Neural Network Models ◽

Network Applications ◽

The Neural Network ◽

Artificial Neural ◽

Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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Artificial Neural Network Applications in Business and Engineering

10.4018/978-1-7998-3238-6 ◽

2021 ◽

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Applications ◽

Artificial Neural ◽

Neural Network Applications

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The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network

Sustainability ◽

10.3390/su12187433 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7433

Author(s):

Danny Chi Kuen Ho ◽

Eve Man Hin Chan ◽

Tsz Leung Yip ◽

Chi-Wing Tsang

Keyword(s):

Neural Network ◽

United States ◽

Artificial Neural Network ◽

Developing Countries ◽

Trade Flow ◽

The United States ◽

Extended Gravity ◽

Belt And Road ◽

Artificial Neural ◽

The Belt And Road

In 2013, China announced the Belt and Road Initiative (BRI), which aims to promote the connectivity of Asia, Europe, and Africa and deepen mutually beneficial economic cooperation among member countries. Past studies have reported a positive impact of the BRI on trade between China and its partner countries along the Belt and Road (B&R). However, less is known about its effect on the sectoral trade between the B&R countries and countries that show little support of the BRI. To address that gap, this study examines the changing patterns of clothing imports by the United States (US) from China and 14 B&R countries in Asia. An extended gravity model with a policy variable BRI is built to explain bilateral clothing trade flow. A panel regression model and artificial neural network (ANN) are developed based on the data collected from 1998 to 2018 and applied to predict the trade pattern of 2019. The results show a positive effect of the BRI on the clothing exports of some Asian developing countries along the B&R to the US and demonstrate the superior predictive power of the ANN. More research is needed to examine the balance between economic growth and the social and environmental sustainability of developing countries and to apply more advanced machine learning algorithms to examine global trade flow under the BRI.

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