改进模糊神经网络的校直行程预测  

Straightening stroke prediction based on the improved fuzzy neural network

作　　者：陈明灯 郝建军 杨治刚 叶志雄 梁建 CHEN Mingdeng;HAO Jianjun;YANG Zhigang;YE Zhixiong;LIANG Jian(College of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学机械工程学院,重庆400054

出　　处：《重庆理工大学学报（自然科学）》2023年第1期111-119,共9页Journal of Chongqing University of Technology：Natural Science

摘　　要：针对目前轴类校直机校直行程预测精度低、耗时长的问题,提出一种改进模糊神经网络结构。将模糊系统与神经网络相结合,在网络结构中设计承接层,能对校直行程历史数据进行反馈,增强网络数据处理能力;将影响校直行程的相关因素作为参考指标,把实时校直成功数据作为模型输入,校直行程作为模型输出。与传统预测方法进行比较,实验结果表明:改进模糊神经网络的实际值与预测值相对误差为1.65%,提高了校直行程预测精度和校直效率。Aiming at the problems of low accuracy and long time consumption of straightening stroke prediction of shaft straightening machines, this paper proposes an improved fuzzy neural network model. The system framework of the prediction model is a combination of the fuzzy system and the neural network, and a connection layer is designed in the network structure, which can give feedback to the historical data of the straightening stroke and enhance the network data processing ability. Relevant factors affecting the straightening stroke are taken as reference indexes, successful real-time straightening data are taken as model input, and the straightening stroke is taken as model output. Compared with the traditional prediction methods, the experimental results show that the relative error between the actual value and the predicted value of the improved fuzzy neural network is 1.65%, achieving an improvement in the prediction accuracy of the straightening stroke and the straightening efficiency.

关 键 词：校直机 校直行程预测 改进模糊神经网络 承接层 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]