摘要
针对机械设备磨损难以预测问题,提出RBF神经网络预测模型,并结合粒子群算法优化模型参数。利用变速箱型号为SG135-2系列的K727840ZW齿轮磨损实验输入-输出数据,通过基于粒子群算法的RBF神经网络建立输出预测模型,并与传统的AR模型、BP神经网络模型及Hermite神经网络模型预测作比较。仿真结果表明,基于粒子群算法的RBF神经网络模型结构简单、预测精度高,验证了所提方法的有效性和实用性。
Aimed at the unpredictability of the mechanical equipment wear,RBF neural network prediction model was put forward,and combined with particle swarm optimization algorithm( PSOA) to optimize the model parameters. The gear wear experimental input-output data of the transmission model for SG135-2 series K727840 ZW was used to establish the output prediction model by RBF neural network based on PSOA,and then compared with the predication of traditional AR model,BP neural network model and Hermite neural network model. The simulation results show that the RBF neural network model based on PSOA has simpler structure and higher prediction precision,and the validity and practicability of the proposed method is verified.
出处
《机床与液压》
北大核心
2016年第3期183-187,共5页
Machine Tool & Hydraulics
基金
国家自然科学基金资助项目(51465055)
自治区自然科学基金资助项目(2014211A010)
国家重点实验室开放课题(Sklms2014005)
关键词
RBF神经网络
粒子群算法
齿轮磨损
预测
RBF neural network
Particle swarm optimization algorithm
Gear wear
Prediction
