The incremental improved Back-Propagation （BP） neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward, which can solve the problems existing in the process ...The incremental improved Back-Propagation （BP） neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward, which can solve the problems existing in the process of calcinations for ammonium diuranate （ADU） by microwave heating, such as long testing cycle, high testing quan- tity, difficulty of optimization for process parameters. Many training data probably were offered by the way of increment batch and the limitation of the system mem- ory could make the training data infeasible when the sample scale was large. The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ADU, and the incremental improved BP neural network is very useful in overeoining the local minimum problem, finding the global optimal solution and accelerating the convergence speed.展开更多
基金supported by the National Natural Science Foundation of China (No.50734007)Technology Project of Yunnan Province (No.2007GA002)
文摘The incremental improved Back-Propagation （BP） neural network prediction model using the Levenberg-Marquardt algorithm based on optimizing theory is put forward, which can solve the problems existing in the process of calcinations for ammonium diuranate （ADU） by microwave heating, such as long testing cycle, high testing quan- tity, difficulty of optimization for process parameters. Many training data probably were offered by the way of increment batch and the limitation of the system mem- ory could make the training data infeasible when the sample scale was large. The prediction model of the nonlinear system is built, which can effectively predict the experiment of microwave calcining of ADU, and the incremental improved BP neural network is very useful in overeoining the local minimum problem, finding the global optimal solution and accelerating the convergence speed.
