Combined with the practical situation of Nosiheptide fermentation process

a weighted radial basis function neural network (WRBFNN) based biomass soft sensor modeling method is presentedBased on the unstructured model of Nosiheptide fermentation process

the secondary variables were selected according to the implicit function existence theorem

which made the selection theoretically strictAccording to the characteristics that biomass could vary in a wide range

the error function of the traditional RBFNN was improvedEach batch sample was selfadaptively weighted according to their predicting ability to the predicted object

and then WRBFNN was used to develop the biomass soft sensor modelThe testing results showed the effectiveness of the presented approach.