A Neural Network with Minimal Structure for Maglev System Modeling and Control
Résumé
The paper is concerned with the determination of a minimal structure of a one hidden layer perceptron for system identification and control. Structural identification is a key issue in neural modeling. Decreasing the size of the neural networks is a way to avoid overfitting and bad generalization and leads moreover to simpler models which are required for real time applications, particularly in control. A learning algorithm and a pruning method both based on a criterion robust to outliers are presented. Their performances are illustrated on a real example, the inverse model identification of a MagLev system, which is nonlinear, dynamical and rapid. This inverse model is used in a feedforward neural control scheme. Very satisfactory approximation performances are obtained for a network with very few parameters.
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