Abstract: For forecasting the key technology indicators (grinding granularity and mill discharge velocity) of grinding process, an adaptive soft-sensor modeling method based on wavelet neural network (WNN) was proposed. The assistant variables of the soft-sensor model are selected by analyzing the technique characteristic of the grinding process. The structure parameters (scaling factors and translation factors of the wavelet functions, connections weights) of the WNN are optimized by the shuffled frog leaping algorithm (SFLA) to realize the nonlinear mapping between input and output variables of the discussed softsensor model. Model migration strategy and input-output space bias correction (IOSBC) method are adopted to realize the on-line adaptive revision of soft-sensor model. The simulation results show that the proposed model can significantly enhance the predictive accuracy and robustness of the technicaland-economic indexes and satisfy the real-time control requirements of the grinding process. Key words：

Key words: grinding process, soft-sensor, wavelet neural network (WNN), shuffled frog leaping algorithm(SFLA), model migration