Optimal Forecast Control of the Penicillin Fermentation Based on Boltzmann Machines

Based on dynamic neural network-Boltzmann machines, a new optimal forecast control method of penicillin fermentation processes is proposed. First, according to the input and output data of the fed-batch fermentation processes, the generalized predictive control (GPC) model is built by system identification tools. Secondly, adopting the dynamic neural network-Boltzmann machine as an optimal controller, combining with the penicillin fermentation process and its GPC model, this paper structures the receding optimization closed loop. This method implements the three taches of GPC: multistep prediction, recursive optimization, feedback emendation. Simulation experiment results show that using this method the outcome concentration can increase twenty-five percent, and this control method is effectual.