With an optimal network topology and tuning of hyperparameters, artificial neural networks (ANNs) may be trained to learn a mapping from low level audio features to one or more higher-level representations. Such artificial neural networks are commonly used in classification and regression settings to perform arbitrary tasks. In this work we suggest repurposing autoencoding neural networks as mu...

This paper presents an alternative approach to time series forecasting, through use of artificial neural networks (ANNs), a relatively new concept in hydrological research. Box and Jenkins ARMAX (autoregressive moving average with exogenous inputs) models have been widely used in modeling various time series with satisfactory results. This study shows that ANNs can produce comparable, to ARMAX,...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...

In medicine at present, neural networks are a ‘hot’ research area, particularly in cardiology, radiology, urology, oncology etc. In the area of computer science, this new technology has been accepted. The purpose of a neural network is to map an input into a desired output. Combining neurons into layers permits artificial neural networks to solve highly complex classification problems. The vari...

In this thesis, artificial neural networks (ANNs) are used for prediction of financial and macroeconomic time series. ANNs build internal models of the problem and are therefore suited for fields in which accurate mathematical models cannot be formed, e.g. meteorology and economics. Feedforward neural networks (FFNNs), often trained with backpropagation, constitute a common type of ANNs. Howeve...

This paper is focused on the prediction and forecast of climate time series, particularly useful for planning and management of the power grid, by artificial neural networks. An appropriate prediction and forecast of climate variables, indeed, improves the overall efficiency and performance of renewable power plants connected to the power grid. On such a basis, the application of suitable Artif...

Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects be...

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and...

Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, ...