LEADER 04402nam 2201033z- 450 001 9910557103703321 005 20210501 035 $a(CKB)5400000000041010 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69213 035 $a(oapen)doab69213 035 $a(EXLCZ)995400000000041010 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (202 p.) 311 08$a3-03943-334-2 311 08$a3-03943-335-0 330 $aArtificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids. 606 $aHistory of engineering and technology$2bicssc 610 $aadaptive neuro-fuzzy inference system 610 $aartificial intelligence 610 $aasynchronous motor 610 $aautomatic generation control 610 $aback propagation algorithm 610 $aCNN-LSTM 610 $acognitive meters 610 $acondition assessment 610 $aconvolutional neural network 610 $acurrent balancing algorithm 610 $aData Envelopment Analysis (DEA) 610 $adecision optimization 610 $adeep learning 610 $adeep reinforcement learning 610 $adistribution network equipment 610 $adroop curve 610 $aelectric load forecasting 610 $aelectricity forecasting 610 $aenergy Internet 610 $aerror differentiation 610 $afrequency regulation 610 $aFuzzy Analytical Network Process (FANP) 610 $afuzzy iteration 610 $afuzzy logic 610 $afuzzy neural network control 610 $aFuzzy Theory 610 $aknowledge embed 610 $alevel-shifted SPWM 610 $alinear active disturbance rejection control 610 $aload disaggregation 610 $along-term forecasting (LTF) 610 $amachine learning 610 $amedium-term forecasting (MTF) 610 $amedium-voltage applications 610 $ameta-heuristic algorithms 610 $amotor drives 610 $amulti information source 610 $amulti-layer perceptron 610 $amultilevel current source inverter 610 $aNILM 610 $anon-dominated sorting genetic algorithm II 610 $anon-technical losses 610 $aparticle swarm optimization 610 $aphase-shifted carrier SPWM 610 $arenewable energy 610 $areserve power 610 $asemi-supervised learning 610 $ashort-term forecasting (STF) 610 $asmart grid 610 $asolar power plant 610 $aSTATCOM 610 $astate machine 610 $athe rate of change of frequency 610 $athermostatically controlled loads 610 $avector control 610 $avery short-term forecasting (VSTF) 615 7$aHistory of engineering and technology 700 $aSimões$b Marcelo Godoy$4edt$01328806 702 $aParedes$b Helmo Kelis Morales$4edt 702 $aSimões$b Marcelo Godoy$4oth 702 $aParedes$b Helmo Kelis Morales$4oth 906 $aBOOK 912 $a9910557103703321 996 $aApplied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids$93038973 997 $aUNINA