Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control

© 2024 by the authors; CC BY-NC-ND license

neural network control; multi-input multi-output temperature system; transient response; temperature uniformity; non-uniform noise; memetic algorithms; particle swarm optimization; direction of arrival estimation; subspace maximum-likelihood; process reliability estimating; fluorochemical engineering process; fuzzy inference system; quality prediction; prognostics and health management; induction motor; finite control set; model predictive torque control; ripple attenuation; fuzzy adaptive theory; exothermic chemical reactors; temperature stabilization; optimal control; optimal reactor productivity; Marshall–Olkin Kumaraswamy; consumer’s risk; group acceptance plan; legged robot; hydraulic drive unit (HDU); BP neural network; PID control; radial basis function neural network (RBFNN); generation performance; local generalization error bound; self-organizing structure method; convergence analysis; fault diagnosis; Meta-ACON; ADSD-gcForest; SDPimage; k-nearest neighbor; outliers; pseudo-neighbors; mutual nearest neighbor; fault detection; process monitoring; wind power; doubly fed induction generator; parameter identification; immune algorithm; genetic algorithm; event-triggered scheduling; Markov jump nonlinear systems(MJNSs); error threshold; partly unknown probabilities; asynchronous filtering; crude oil refining; crude oil hydrotreating; bootstrap aggregated neural networks; multi-objective optimization; back propagation neural network; extended Kalman filter; electric vehicle; state of charge; control systems; hemorrhagic shock; fluid resuscitation; fuzzy logic; closed-loop; fluid resuscitation; hardware-in-loop; traffic light control; deep reinforcement learning; particle swarm optimization; artificial neural network; training algorithm; machine learning; two-level learning phases; n/a