As wavelet neural networks(WNN)algorithm usually has low convergence rate and easily falls into local minimum

an improved wavelet neural networks(IWNN)is proposed to modify the parameters.Firstly

the traditional Sigmoid function is replaced by hyperbolic tangent function for output layer.Secondly

the learning step is selected by adding momentum to the weight adjustment to improve learning efficiency.The proposed IWNN method is used to build soft sensing of light diesel pour point from fluidized catalytic unit(FCCU)main fractionator.Compared with the models of BP and WNN

the results obtained by the IWNN approach showed a better prediction accuracy and generalization capability.Moreover this modeling can be used to guide production efficiently.