关键词: 混合变异, 自适应, 遗传算法, 明胶浓度, 软测量

Abstract: The concentration of gelatin is an important task in gelatin production. Soft measurement is used in measuring the concentration of gelatin in order to resolve the problem of the off-line sampling and monitoring method with low accuracy, a long time lag, measurement error and so on. Wavelet neural network soft measurement model is used for gelatin concentration and mixed mutation with adaptive genetic algorithm is for parameter optimization. 120 groups of data collected from gelatin production process and dealt with normalization are used for simulation, of which 80 groups as a training and 40 as a predicting. The method can enhance the model’s robustness and improve the convergence rate. The results of simulation indicate that the square error of wavelet neural network model is less than 0.01 after 67 iterations and tends to stable. It is proved that the algorithm is feasible for the concentration of gelatin-line measurement.

Key words: mixed mutation, adaptive, genetic algorithm, gelatin concentration, soft measurement