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[1]
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Universal phase shifter regulator system modeling with robust GPC using neural networks for compensation power in transmission line
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International Journal of …,
2022 |
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[2]
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Neural Network based Robust Integrated Autopilot for an Aircraft
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2021 IEEE 6th …,
2021 |
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[3]
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A Survey on Machine Learning Applied to Dynamic Physical Systems
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2019 |
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[4]
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Sensorless Indirect Neural Control of Induction Motor
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2018 |
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[5]
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Design and implementation of a soft computing-based controller for a complex mechanical system
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2016 |
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[6]
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Decoupling control based on PID neural network for deaerator and condenser water level control system
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Control Conference (CCC), 2015 34th Chinese,
2015 |
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[7]
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無人搬運系統建置與路徑導航控制
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2015 |
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[8]
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PID Neural Network Decoupling Control of Deaerator Pressure and Water Level Control System
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2014 IEEE International Conference on Robotics and Biomimetics (ROBIO),
2014 |
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[9]
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Application of PID Neural Network Decoupling Control in Deaerator Pressure and Deaerator Water Level Control System
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AsiaSim 2014.Springer,
2014 |
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[10]
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Using NEAT to Stabilize an Inverted Pendulum
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NULL
2014 |
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[11]
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Increasing Wind Speed Prediction Accuracy Using Hybrid Model with Passive Aggregation
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Journal of Image and Graphics,
2013 |
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[12]
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A New Hybrid Model with Passive Congregation for Stock Market Indices Prediction
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World Academy of Science, Engineering and Technology,
2012 |
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[13]
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SHORT TERM WIND SPEED PREDICTION USING A NEW HYBRID MODEL WITH PASSIVE CONGREGATION
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INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY,
2012 |
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Advances in Automation II
Lecture Notes in Electrical Engineering,
2021
DOI:10.1007/978-3-030-71119-1_1
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[2]
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Neural Network based Robust Integrated Autopilot for an Aircraft
2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA),
2021
DOI:10.1109/ICCCA52192.2021.9666325
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[3]
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Development of neural network for control production process in oil and gas fields
IOP Conference Series: Materials Science and Engineering,
2020
DOI:10.1088/1757-899X/971/4/042069
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[4]
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Sensorless Indirect Neural Control of Induction Motor
2018 IEEE XXVII International Scientific Conference Electronics - ET,
2018
DOI:10.1109/ET.2018.8549639
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[5]
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Decoupling control based on PID neural network for deaerator and condenser water level control system
2015 34th Chinese Control Conference (CCC),
2015
DOI:10.1109/ChiCC.2015.7260169
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[6]
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AsiaSim 2014
Communications in Computer and Information Science,
2014
DOI:10.1007/978-3-662-45289-9_2
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[7]
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PID neural network decoupling control of deaerator pressure and water level control system
2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014),
2014
DOI:10.1109/ROBIO.2014.7090680
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[8]
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Increasing Wind Speed Prediction Accuracy Using Hybrid Model with Passive Aggregation
Journal of Image and Graphics,
2013
DOI:10.12720/joig.1.2.63-67
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