Support Vector Machines (SVM) are believed to be as powerful as Artificial Neural Networks (ANN) in modeling complex problems while avoiding some of the drawbacks of the latter such as local minimæ or reliance on architecture. However, a question that remains to be answered is whether SVM users may expect improvements in the interpretability of their models, namely by using rule extraction meth...

This paper presents an empirical exercise in economic forecast using traditional time series methods, such as ARIMA and unobservable components models (UCM), and artificial neural networks (ANN). We use monthly gross industrial output data for the state of Rio Grande do Sul (Brazil) to perform a comparative exercise and access the relative performance of the different forecasting methods. The r...

An artificial neural network (ANN) is an information-processing paradigm that is designed to emulate some of the observed properties of the mammalian brain. First proposed in the early 1950's it was not until the technology revolution of the 1980's that a multitude of alternative ANN methods were spawned to solve a wide variety of complex real-world problems. Today, the contemporary literature ...

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is...

Artificial neural networks (ANN) are implemented in a large number of applications of science and technology as the technique has become very popular and accepted tool for researchers and scientists. ANN renders realistic advantages such as real time processing, adaptability and training potential over conventional methodologies. In this communication an all inclusive review of ANN for predicti...

In this contribution Artificial Neural Network (ANN) modeling of the hydrocracking process is presented. The input–output data for the training and simulation phases of the network were obtained from the Tehran refinery ISOMAX unit. Different network designs were developed and their abilities were compared. Backpropagation, Elman and RBF networks were used for modeling and simulation of the hyd...

The surface quality of the machined parts is one of the most important product quality characteristics and one of the most frequent customer requirements. In this study, an artificial neural network (ANN) approach for modeling of surface roughness in dry single-point turning of an alloy steel using coated tungsten carbide inserts is presented. The three main cutting parameters consisting of cut...