Title :
Neural network application to linear systems with binary inputs
Author :
Holderbaum, William
Author_Institution :
Reading Univ., UK
Abstract :
The purpose of this paper is to use neural network to control a continuous systems with Boolean inputs. These Boolean input systems have increased in the electric industry. Power supplies include such systems and the power converter represents these. For instance in power electronics the control variable are the switching OFF and ON of components as thyristors or transistors. This method is based on classification of system variations associated with input configurations. The supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. This approach is implemented in simulation to control an electronic circuit.
Keywords :
Runge-Kutta methods; backpropagation; continuous time systems; linear systems; multivariable systems; neural nets; power convertors; power electronics; switching circuits; variable structure systems; Boolean input systems; Runge-Kutta methods; artificial neural network training; binary inputs; continuous systems; control variable; electric industry; electronic circuit; linear systems; multivariable systems; power converter; power electronics; supervised backpropagation algorithm; switching circuits; thyristors; transistors; variable structure systems; Continuous time systems; Control systems; Electric variables control; Electrical equipment industry; Linear systems; Neural networks; Power electronics; Power supplies; Thyristors; Transistors;
Conference_Titel :
Decision and Control, 2003. Proceedings. 42nd IEEE Conference on
Print_ISBN :
0-7803-7924-1
DOI :
10.1109/CDC.2003.1272445