LEADER 04383nam 2201021z- 450 001 9910557103703321 005 20231214133350.0 035 $a(CKB)5400000000041010 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69213 035 $a(EXLCZ)995400000000041010 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$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 electronic resource (202 p.) 311 $a3-03943-334-2 311 $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 & technology$2bicssc 610 $adroop curve 610 $afrequency regulation 610 $afuzzy logic 610 $athe rate of change of frequency 610 $areserve power 610 $asmart grid 610 $aenergy Internet 610 $aconvolutional neural network 610 $adecision optimization 610 $adeep reinforcement learning 610 $aelectric load forecasting 610 $anon-dominated sorting genetic algorithm II 610 $amulti-layer perceptron 610 $aadaptive neuro-fuzzy inference system 610 $ameta-heuristic algorithms 610 $aautomatic generation control 610 $afuzzy neural network control 610 $athermostatically controlled loads 610 $aback propagation algorithm 610 $aparticle swarm optimization 610 $aload disaggregation 610 $aartificial intelligence 610 $acognitive meters 610 $amachine learning 610 $astate machine 610 $aNILM 610 $anon-technical losses 610 $asemi-supervised learning 610 $aknowledge embed 610 $adeep learning 610 $adistribution network equipment 610 $acondition assessment 610 $amulti information source 610 $afuzzy iteration 610 $acurrent balancing algorithm 610 $alevel-shifted SPWM 610 $amedium-voltage applications 610 $amultilevel current source inverter 610 $amotor drives 610 $aphase-shifted carrier SPWM 610 $aSTATCOM 610 $aelectricity forecasting 610 $aCNN?LSTM 610 $avery short-term forecasting (VSTF) 610 $ashort-term forecasting (STF) 610 $amedium-term forecasting (MTF) 610 $along-term forecasting (LTF) 610 $aasynchronous motor 610 $alinear active disturbance rejection control 610 $aerror differentiation 610 $avector control 610 $arenewable energy 610 $asolar power plant 610 $aData Envelopment Analysis (DEA) 610 $aFuzzy Analytical Network Process (FANP) 610 $aFuzzy Theory 615 7$aHistory of engineering & 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