修正型果蝇算法优化GRNN的大梁自动焊障碍预测
Prediction in longeron automatic welding of generalized regression neural network by ameliorated fruit flies optimization algorithm
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摘要: 大梁自动焊时，必须自动避开工件上的筋板、隔板和空洞等障碍物.但因产品的种类多，工件上障碍物的位置存在随机性，难以通过单一的方法进行障碍物预测.针对该问题，利用超声波传感器采集障碍物信息，提出一种修正型果蝇算法优化广义回归神经网络（AFOA-GRNN）的大梁自动焊障碍物预测模型.该方法在传统果蝇算法中引入信息素和灵敏度两个因子，改进了寻优策略和果蝇位置的替换方式，对GRNN进行参数优化，进行大梁自动焊障碍物的预测.结果表明，建立的修正型AFOA-GRNN预测模型相比于FOA-GRNN，训练速度更快，预测精度更高.Abstract: As longeron automatic welding, the obstacles in welding area such as stiffening plates, partitions and holes must be automatically avoided. The location of the obstacles on the workpiece are random due to the products are varied and the low-precision of the clamping, we hardly predict the obstacles in a single way. To solve this problem, by the use of ultrasonic sensor collecting the date of the obstacles, raising a prediction model in longeron automatic welding based on the ameliorated fruit flies algorithm optimization generalized regression neural network(AFOA-GRNN). The method by introducing the information element and the sensitivity of the two factors in traditional fruit flies algorithm, improved optimization strategies and location updating of fruit flies position, to optimize the parameters of the generalized neural network, to predict the obstacles in longeron automatic welding. Experiments show that correction type AFOA-GRNN prediction model compared to FOA-GRNN, training speed is faster, and the predicting precision is higher.
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