Title :
Neural network for constrained predictive control
Author :
Quero, J.M. ; Camacho, E.F. ; Franquelo, L.G.
Author_Institution :
Dept. de Ingeniera Electron., Seville Univ., Spain
fDate :
9/1/1993 12:00:00 AM
Abstract :
Presents the way in which optimization neural nets can be used to implement generalized predictive control for systems with constrained inputs and outputs. A set of recursive formulas to obtain the net parameters from the process parameters for first-order systems is given. The results obtained by simulation and electronic implementation of the neural net are presented
Keywords :
Hopfield neural nets; optimisation; predictive control; recursive functions; constrained inputs; constrained outputs; constrained predictive control; first-order systems; net parameters; optimization neural nets; process parameters; recursive formulas; Active filters; Approximation methods; Chebyshev approximation; Circuit theory; Filtering theory; IIR filters; Jacobian matrices; Neural networks; Predictive control; Speech;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
