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    <title>Ergebnis für "artificial neural networks"</title>
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      <title>Improving the accuracy of modeling the trend and seasonal components in the medium-term forecasting of the consumed electric power from the integrated power system</title>
      <pubDate>Tue, 24 Mar 2026 02:01:48 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/systemreorg-article-446</link>
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      <author>Martyniuk A.</author>
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      <dc:date>2012</dc:date>
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      <title>Prediction of hydraulic resistance coefficient using an ensemble neural network algorithm</title>
      <pubDate>Sun, 08 Feb 2026 07:59:15 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/es-journalinua-article-351693</link>
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      <author>Khodnevych, Yaroslav</author>
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      <dc:date>2025</dc:date>
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      <title>Розробка штучної нейронної мережі для інформаційно-вимірювальної системи контролю геометричних розмірів енергетичного обладнання</title>
      <pubDate>Wed, 24 Sep 2025 17:34:10 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/oai:www.systemre.org:article-887</link>
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      <author>Kacprzyk, Janusz</author>
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      <title>APPLICATION OF AN ARTIFICIAL NEURAL NETWORK FOR PREDICTING THE THERMAL STATE  OF AN AIR-SOIL HEAT EXCHANGER</title>
      <pubDate>Wed, 17 Sep 2025 09:27:37 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/veorgua-article-537</link>
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      <author>Basok , B.</author>
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      <title>HYBRID APPROACH TO MAXIMUM POWER POINT TRACKING IN PHOTOELECTRIC SYSTEMS</title>
      <pubDate>Wed, 17 Sep 2025 09:27:36 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/veorgua-article-532</link>
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      <title>Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions</title>
      <pubDate>Thu, 17 Jul 2025 11:50:13 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/eiekhpieduua-article-302819</link>
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      <title>Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique</title>
      <pubDate>Thu, 17 Jul 2025 11:49:23 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/eiekhpieduua-article-263257</link>
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      <title>Optimal performance assessment of intelligent controllers used in solar-powered electric vehicle</title>
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      <title>Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks</title>
      <pubDate>Thu, 17 Jul 2025 11:49:06 +0000</pubDate>
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      <title>Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks</title>
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      <link>https://harvester.nas.gov.ua/Record/eiekhpieduua-article-256603</link>
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      <title>Fault diagnosis in a five-level multilevel inverter using an artificial neural network approach</title>
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      <title>Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid</title>
      <pubDate>Thu, 17 Jul 2025 11:48:41 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/eiekhpieduua-article-242511</link>
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      <author>Sahraoui, H.</author>
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      <title>New application of artificial neural network-based direct power control for permanent magnet synchronous generator</title>
      <pubDate>Thu, 17 Jul 2025 11:48:37 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/eiekhpieduua-article-239155</link>
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      <author>Akkouchi, K.</author>
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      <dc:date>2021</dc:date>
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      <title>INVESTIGATION OF A SENSORLESS WATER SUPPLY SYSTEM FOR EXCESS ENERGY REDISTRI-BUTION FROM WIND TURBINE</title>
      <pubDate>Thu, 17 Jul 2025 11:39:53 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/veorgua-article-511</link>
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      <author>Zemlianukhina, H.</author>
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      <dc:date>2025</dc:date>
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      <title>Do we need a more sophisticated multilayer artificial neural network to compute roughness coefficient?</title>
      <pubDate>Thu, 17 Jul 2025 11:19:26 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/es-journalinua-article-297285</link>
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      <author>Khodnevych, Yaroslav V.</author>
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      <dc:date>2023</dc:date>
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      <title>Data arrangements to train an artificial neural network within solving the tasks for calculating the Chézy roughness coefficient under uncertainty of parameters determining the hydraulic resistance to flow in river channels</title>
      <pubDate>Thu, 17 Jul 2025 11:18:50 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/es-journalinua-article-260694</link>
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      <author>Khodnevych, Yaroslav V.</author>
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      <dc:date>2022</dc:date>
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      <title>Application of neuro evolution tools in automation of technical control systems</title>
      <pubDate>Thu, 17 Jul 2025 10:06:29 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/pp_isofts_kiev_ua-article-448</link>
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      <author>Doroshenko, А.Yu.</author>
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      <dc:date>2021</dc:date>
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      <title>Neural networks’ learning process acceleration</title>
      <pubDate>Thu, 17 Jul 2025 10:06:19 +0000</pubDate>
      <link>https://harvester.nas.gov.ua/Record/pp_isofts_kiev_ua-article-423</link>
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      <author>Katerynych, L.</author>
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      <title>Distributed implementation of neuroevolution of augmenting topologies method</title>
      <pubDate>Thu, 17 Jul 2025 09:35:32 +0000</pubDate>
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