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210512 ||| eng |
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|a books978-3-03943-335-3
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|a 9783039433346
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|a 9783039433353
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| 100 |
1 |
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|a Simões, Marcelo Godoy
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| 245 |
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0 |
|a Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (202 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a machine learning
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| 653 |
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|a droop curve
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| 653 |
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|a automatic generation control
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| 653 |
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|a back propagation algorithm
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| 653 |
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|a linear active disturbance rejection control
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| 653 |
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|a Data Envelopment Analysis (DEA)
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| 653 |
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|a Fuzzy Theory
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| 653 |
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|a multilevel current source inverter
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| 653 |
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|a renewable energy
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| 653 |
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|a adaptive neuro-fuzzy inference system
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| 653 |
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|a motor drives
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| 653 |
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|a frequency regulation
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| 653 |
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|a current balancing algorithm
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| 653 |
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|a NILM
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| 653 |
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|a energy Internet
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| 653 |
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|a condition assessment
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| 653 |
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|a multi-layer perceptron
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| 653 |
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|a deep learning
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| 653 |
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|a decision optimization
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| 653 |
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|a knowledge embed
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a asynchronous motor
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| 653 |
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|a fuzzy iteration
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| 653 |
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|a medium-voltage applications
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| 653 |
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|a deep reinforcement learning
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| 653 |
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|a fuzzy neural network control
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| 653 |
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|a CNN-LSTM
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| 653 |
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|a state machine
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| 653 |
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|a Fuzzy Analytical Network Process (FANP)
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| 653 |
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|a meta-heuristic algorithms
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| 653 |
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|a distribution network equipment
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| 653 |
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|a long-term forecasting (LTF)
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| 653 |
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|a phase-shifted carrier SPWM
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| 653 |
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|a level-shifted SPWM
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| 653 |
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|a medium-term forecasting (MTF)
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| 653 |
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|a thermostatically controlled loads
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| 653 |
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|a STATCOM
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| 653 |
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|a smart grid
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| 653 |
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|a non-technical losses
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| 653 |
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|a the rate of change of frequency
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| 653 |
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|a cognitive meters
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| 653 |
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|a very short-term forecasting (VSTF)
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| 653 |
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|a electric load forecasting
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| 653 |
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|a fuzzy logic
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| 653 |
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|a error differentiation
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| 653 |
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|a semi-supervised learning
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| 653 |
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|a artificial intelligence
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| 653 |
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|a non-dominated sorting genetic algorithm II
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| 653 |
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|a short-term forecasting (STF)
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| 653 |
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|a load disaggregation
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| 653 |
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|a convolutional neural network
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| 653 |
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|a electricity forecasting
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| 653 |
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|a vector control
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| 653 |
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|a reserve power
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| 653 |
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|a solar power plant
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| 653 |
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|a multi information source
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| 700 |
1 |
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|a Paredes, Helmo Kelis Morales
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| 700 |
1 |
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|a Simões, Marcelo Godoy
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| 700 |
1 |
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|a Paredes, Helmo Kelis Morales
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-03943-335-3
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/69213
|z DOAB: description of the publication
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/2985
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 900
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| 082 |
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|a 000
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| 082 |
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|a 333
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|a 580
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|a 700
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|a 600
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|a 620
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|a Artificial 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.
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