摘要
针对BP神经网络预测工件表面粗糙度精度不高的问题,提出了一种基于遗传算法优化的BP神经网络预测方法。首先用遗传算法对BP神经网络的初始权值、阈值进行全局寻优,然后对优化的BP神经网络进行训练、预测。通过MATLAB进行了粗糙度预测仿真验证。结果表明:优化的BP神经网络比未优化的BP神经网络具有更高的预测精度。
A BP neural network prediction method of roughness with genetic algorithm( GA) is proposed to solve the problem of low prediction precision. In the present method,GA is firstly used to determine the initial weights and threshold by global optimization,and then the optimal BP neural network is used to train and predict the roughness. A simulation of the roughness prediction is executed with MATLAB. The simulation results show that the prediction accuracy of roughness with BP neural network is higher than that the un-optimal BP neural network.
出处
《机械科学与技术》
CSCD
北大核心
2015年第5期729-732,共4页
Mechanical Science and Technology for Aerospace Engineering
基金
西北工业大学基础研究基金项目(JC20110215)
陕西省自然科学基础研究计划项目(2013JM7001)
西北工业大学2012校级"新人新方向"项目资助
关键词
遗传算法
BP神经网络
表面粗糙度
预测方法
backpropagation algorithms
BP neural network
computer simulation
flowcharting
forecasting
genetic algorithms
global optimization
MATLAB
mean square error
prediction
roughness
schematic diagrams
steepest descent method
surface roughness
