基于混合神经网络与遗传算法方法的注塑参数优化

建立了基于混合神经网络与遗传算法方法的注塑工艺参数优化系统，用Matlab语言编制了应用程序，对神经网络的参数预测与遗传算法的优化过程进行求解。将网络预测结果与CAE模拟结果进行比较和误差分析，显示出BP网络的稳定性和可靠性；优化结果经CAE模拟和实验验证，证明是正确的，表明基于混合神经网络与遗传算法方法的注塑工艺参数优化方法是可行的。

Optimization of Injection Parameters Based on Hybrid Neural Network and Genetic Algorithm

In this paper, an optimization system is established based on a hybrid neural network and genetic algorithm approach. The application program is compiled in Matlab engineering computing language, which is used in calculating the parameter value predicted by neural network and the result of genetic algorithm optimization. The comparison and error analysis has been carried out between the results predicted by network and CAE simulated results, which shows that the BP network is stable and reliable. The optimized outcome, after verified by CAE simulation and tested by experiment, has been proved to be correct. It has been indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible.