Some key parameters in the fermentation process of marine protease (MP) are difficult to be detected online. There is the existence of large time-delay and easily stained bacteria in off-line measurement. A soft sensor modeling method based on the improved PSO-RBFNN in the MP fermentation process was proposed. Firstly

exponential decreasing inertia weight (EDIW) strategy was used to improve PSO algorithm

and overcome the disadvantages that PSO with fixed inertia weight and adaptive inertia weight is easy to fall into the local minimum

the convergence rate is slow in late evolution and the global search ability is weak. Then the improved PSO algorithm was used to optimize the connection weight of RBFNN

and the RBFNN topology was successively determined. Finally

the RBFNN soft sensor model was constructed according to the input/output vector of MP fermentation process. The simulation results showed that the training time of the EDIW-PSO-RBFNN model was reduced by at least (about) 40%

and the prediction accuracy of model was improved by more than 3%.