This work presents a methodology for optimizing heat-integrated crude oil distillation
systems. Part I of this three-part series presents a modeling strategy where artificial neural …

Neuroevolution methods evolve the weights of a neural network, and in some cases the
topology, but little work has been done to analyze the effect of evolving the activation …

Background: Since high-frequency data have become available, an unbiased volatility
estimator, ie realized variance (RV) can be computed. Commonly used models for RV …

The article considers the possibility of using neural networks for the short-term forecasting of
electricity prices in the day-ahead market (DAM) based on factors strictly determined for the …

The use of statistics from standard SCADA signals as inputs to monitor fatigue loads in wind
turbines is a promising alternative to its continuous measurement with extra sensors and is …

В статье рассматривается возможность применения нейронных сетей для
краткосрочного прогнозирования цен на электроэнергию на «рынке на сутки …

This study presents the development of novel artificial neural networks (ANN) models for
assessment of best management practices (BMPs) for controlling runoff and sediment yield …

In recent years, the debate on market efficiency has shifted to providing alternate forms of
the hypothesis, some of which are testable and can be proven false. This thesis examines …

This book will provide a comprehensive overview of business analytics, for those who have
either a technical background (quantitative methods) or a practitioner business background …

Celem artykułu jest porównanie błędu prognoz indeksu WIG20 uzyskanych za pomocą
tradycyjnej metody statystycznej oraz metod uczenia maszynowego: metody wektorów …