Guided neural network learning using a fuzzy controller, with applications to textile spinning

This paper applies neural networks to build a metamodel of the relation between key input parameters and output performance measures of a simulated textile spinning plant. It investigates two different neural network estimation algorithms, namely back-propagation and an algorithm incorporating a fuzzy controller for the learning rate. According to experience, both algorithms are capable of providing high-quality predictions. In addition, results obtained using a fuzzy controller for the learning rate suggest a significant potential for speeding up the training process.