  A method based on Bayesian neural networks and genetic algorithm is proposed
to control the fermentation process. The relationship between input and output
variables is modelled using Bayesian neural network that is trained using
hybrid Monte Carlo method. A feedback loop based on genetic algorithm is used
to change input variables so that the output variables are as close to the
desired target as possible without the loss of confidence level on the
prediction that the neural network gives. The proposed procedure is found to
reduce the distance between the desired target and measured outputs
significantly.

Computational Engineering, Finance, and Science;Neural and Evolutionary Computing