Title :
Application of Neural Network to Hybrid Systems With Binary Inputs
Author :
Holderbaum, William
Author_Institution :
Univ. of Reading, Reading
fDate :
7/1/2007 12:00:00 AM
Abstract :
Boolean input systems are in common used 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 on and off of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical 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. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.
Keywords :
Boolean functions; asynchronous machines; backpropagation; continuous time systems; control system synthesis; discrete systems; electricity supply industry; neurocontrollers; nonlinear control systems; power convertors; power system control; switching circuits; Boolean input systems; artificial neural network; binary inputs; continuous systems; control system design; control variable; electric industry; electrical system control; hybrid systems; induction machine; input configuration; network training; neural network control; nonlinear system; power converter; power electronics; power supplies; supervised backpropagation algorithm; switching circuit; thyristors; transistors; Artificial neural networks; Continuous time systems; Control systems; Electric variables control; Electrical equipment industry; Neural networks; Power electronics; Power supplies; Thyristors; Transistors; Boolean control; linear system; neural network (NN); Algorithms; Computer Simulation; Decision Support Techniques; Feedback; Linear Models; Logistic Models; Neural Networks (Computer); Signal Processing, Computer-Assisted;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.899181