摘要
将遗传算法和神经网络两种技术相结合,建立遗传-神经网络插补模型以实现对复杂型面零件插补,该模型兼具神经非线性映射能力和遗传算法快速、收敛学习能力等性能。通过实验分析,验证了遗传-神经网络数控插补的可行性,该方法能够提高复杂型面零件插补的精度及速度。
A combination of genetic algorithm and neural network techniques are used.Modeling the genetic-neural network interpolation was carried out in order to achieve the interpolation for complex surface parts.The model was combined of neural nonlinear mapping ability and genetic algorithm fast convergence learning ability and other properties.An experimental analysis is carried out to verify feasible of genetic-neural network numerical control(NC)interpolation,this method can improve the accuracy and speed of complex surface parts interpolation.
作者
程一夫
王凯
薛会民
CHENG Yifu;WANG Kai;XUE Huimin(School of Mechanical&Electronical Engineering,Hebei University of Engineering,Handan Hebei 056038,China)
出处
《机床与液压》
北大核心
2020年第5期112-114,共3页
Machine Tool & Hydraulics
基金
国家自然基金青年基金项目(51807047)。
