Using genetic algorithms to design a control strategy of an industrial process

In this paper a methodology is presented to design a control strategy to optimise a complex spinning (fibre-yarn) production process, using a neural network combined with genetic algorithms. The neural network is used to model the process, with the machine setpoints and raw fibre quality parameters as input, and with the yarn tenacity and elongation as output. Genetic algorithms are used in two ways: to optimise the architecture and the underlying parameters of the neural network, in order to achieve the most effective model of the production process; to obtain setpoint values and raw material characteristics for an optimal quality of the spinned yarns. (C) 1998 Elsevier Science Ltd. All rights reserved.