Overfitting is one of the important problems that restrain the application of neural network. The traditional OBD (Optima Brain Damage) algorithm uses information of second derivative to calculate sensitivities and overfitting is restrained by deleting low-sensitivity weight. However

training of the network is needed repeatedly and its efficiency is low. In this paper

the Marquardt algorithm is incorporated into the OBD algorithm

so that a new method for pruning network--Dynamic Optimal Brain Damage (DOBD) is introduced. This algorithm simplifies the network and obtains good generalization through deleting weight parameters with low sensitivities dynamically while training at the same time. An approximate method through which sensitivities can be calculated during training with little computation and a rule that uses lower limit of sensitivity to delete the unnecessary weights are presented. The new algorithm is applied to the model of estimating stabilizer gasoline RVP (Reid Vapor Pressure) for FCC (Fluidized Catalytic Cracking) unit in refinery and is compared with OBD and the non-pruning method. It is shown that DOBD can avoid overfitting effectively and has a much faster convergence speed than OBD algorithm.