A neural network with minimal structure for maglev system modeling and control

Abstract

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 a neural network 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 criteria 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 fast. This inverse model is used in a feedforward neural control scheme. Very satisfactory approximation performances are obtained for a network with very few parameters.