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Application of neural network to hybrid systems with binary inputs
1Department of Electronic Engineering, School of Systems Engineering, The University of Reading, Whiteknights, Reading RG6 6AY, U.K. w.holderbaum@rdg.ac.uk
IEEE Transactions on Neural Networks
|August 3, 2007
Summary
This study introduces a novel neural network (NN) approach for controlling continuous systems with Boolean inputs, commonly found in power electronics. The method effectively classifies system variations, enabling precise control of electrical systems like induction machines.
Area of Science:
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background:
- Boolean input systems are prevalent in the electric industry, particularly in power supplies and power converters.
- Control variables in power electronics often involve the switching of components like thyristors and transistors.
Purpose of the Study:
- To develop and present a neural network (NN) based method for controlling continuous systems that utilize Boolean inputs.
- To demonstrate the application of this NN control strategy on a nonlinear system and an electrical system comprising an induction machine and its power converter.
Main Methods:
- Utilizing artificial neural networks (NNs) trained with the supervised backpropagation algorithm.
- Classifying system variations based on different input configurations to enable control.
- Implementing the control system design on a nonlinear system for validation.
Main Results:
- Successfully trained neural networks to control continuous systems with Boolean inputs.
- Demonstrated the effectiveness of the NN control method on a complex electrical system, including an induction machine and power converter.
- Validated the design procedure for control systems using this approach.
Conclusions:
- The proposed neural network method offers a viable solution for controlling continuous systems with Boolean inputs.
- This approach has practical implications for enhancing the control of electrical systems in the power industry.
- The backpropagation algorithm effectively trains NNs for this specific control application.
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