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Integration of solar generation into power networks can negatively affect the performance of next generation smart energy grids. Rapidly changing output power of this kind is unpredictable and thus one of the solutions is to predict it by computational intelligence techniques. The stochastic component of solar radiation is highly non-linear in nature because of many factors including time of the year,...
The purpose of this study is the prediction of Standard & Poor's (S&P500) trends (ups and downs) with macroeconomic variables, technical indicators, and investor moods using k-NN algorithm and probabilistic neural networks. More precisely, eleven economic factors, twelve technical indicators and four measures of investor's mood were selected as potential predictive variables. Then, the Granger...
This paper investigates the use of ensembles of artificial neural networks in predicting long-term Internet traffic. It discusses a method for collecting traffic information based on flows, obtained with the NetFlow protocol, to build the time series. It also proposes four traffic forecasting models based on ensembles of TLFNs (Time-Lagged FeedFoward Networks), each one differing from the others by...
Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build...
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