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14.743  Articles
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Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series o... see more

The method of improvement the process of decision support to improve management of heat supplies’ modes through the developmentof prediction heat consumption model of the social and public sector building is proposed. The object of the study is the proces... see more

Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series o... see more

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered... see more

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in ... see more

Simultaneous flow forecasting using multi-input multi-output (MIMO) processes is an efficient technique for accurate flow forecasting on river systems. The present study demonstrates the capability of radial basis function neural networks (RBFNN) incorpor... see more

This study examines the model development of the drying process of corn grains as a dynamic system. The appropriate use of a dynamic model for the drying process of corn grains could lead to an effective method for optimizing the system. The optimal contr... see more

Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data... see more

The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a m... see more

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