Guided Neural Network Learning Using a Fuzzy Controller, with Applications to Textile Spinning

We apply neural networks to build a metamodel of the relation between key input parameters and output performance measures of a simulated textile spinning plant. We investigate two different neural network estimation algorithms, namely back‐propagation and an algorithm incorporating a fuzzy controller for the learning rate. According to our 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.