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LVL 1 CONTROL (PLC) - Coggle Diagram
LVL 1 CONTROL (PLC)
ML for tuning model predictive controllers
Neural Networks (NN) - The regressor chosen to approximate the human-learned cost function is specified as a Neural Network.
Optimization Algorithms- optimization of tuning the MPC controller
Machine Learning (ML) - to approximate the human-learned cost function.
Heavy Media Coal Separation Process Based on Deep Learning Model
Artificial Neural Networks - for coal seperation optimisation
Ant Colony Algorithm - complex radial basis network structures
Deep Learning Model (ACO-RBF) - learns heavy duty coal seperation techniques and controls the PID
Statistical Process Control with Intelligence Based on the Deep Learning Model
Deep Learning - learns hierarchical data representations
Monte Carlo Simulations - generates random smaples
Multilayer Bi-LSTM - statistical process control pattern identification
Robust nonlinear model predictive control
multi-stage Nonlinear Model Predictive Control (NMPC) - generate data pairs
Deep neural networks - learn the robust NMPC policy
AI-Based Feedback Control Applicable to Process Control Systems
Artificial Neural Networks - used as controllers
Reference Model - provides the teaching signal for the ANN controler
ML-based operational control framework for reducing energy
Density-Based spatial clustering of applications with noise (DBSCAN) -clustering
Gradient Boosting Machines -create several high- accuracy regression models
classical and recent PID tuning methods and their applications in control systems
Fuzzy Logic - manupilates the PID control setpoint