pH Modeling of Fermentation Process Based on Improved Dynamic Recurrent Neural Networks

Alcohol fermentation process is a non-linear and time-varying dynamic process.On the one hand,modeling of pH with conventional identification methods is impossible to accurately describe the dynamic characteristics.On the other hand,weight learning method of conventional neural network,the gradient descent method,is easy to fall into local minima,and is slow in the training process.To solve these problems,pH modeling fermentation process based on improved dynamic recurrent neural networks is studied.Experiments show the validity of the algorithm in pH modeling.Filtering is introduced to the dynamic recurrent neural networks to learn the weight of the recurrent neural networks.It can solve the problems in the weight learning of conventional network effcetively.Experiments show the validity of the algorithm in pH modeling.