Title :
Constrained Hopfield neural network for real-time predictive control
Author :
Quero, J.M. ; Janer, C.L. ; Franquelo, L.G.
Author_Institution :
Dipartimento de Ingenieria de Sistemas y Autom., Seville Univ., Spain
Abstract :
The hardware implementation of an optimization network with restrictions to perform real-time generalized predictive control (GPC) is described. The use of a space-efficient stochastic architecture allows a realization on a programmable logic device. As a result a programmable neural chip coprocessor that solves optimization problems subject to restrictions has been developed. Expressions for network parameters are provided to implement GPC. An adaptive controller is achieved using RAM memories to store the network parameters. Experimental results from a simple implementation of the controller are included
Keywords :
Hopfield neural nets; coprocessors; neural chips; neural net architecture; optimisation; predictive control; process control; real-time systems; RAM memories; adaptive controller; constrained Hopfield neural network; hardware implementation; optimization network; optimization problems; programmable logic device; programmable neural chip coprocessor; real-time generalized predictive control; space-efficient stochastic architecture; Artificial neural networks; Computer networks; Constraint optimization; Coprocessors; Cost function; Hopfield neural networks; Neural network hardware; Neural networks; Predictive control; Stochastic processes;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1994. IECON '94., 20th International Conference on
Conference_Location :
Bologna
Print_ISBN :
0-7803-1328-3
DOI :
10.1109/IECON.1994.398074
