文章摘要
王雅,孙耀宁,李瑞国.基于粒子群算法的RBF神经网络齿轮磨损预测[J].机床与液压,2016,44(3):183-187
基于粒子群算法的RBF神经网络齿轮磨损预测
Gear Wear Prediction of RBF Neural Network Based on Particle Swarm Optimization Algorithm
  
DOI:10.3969/jissn1001-3881201603045
中文关键词: RBF神经网络  粒子群算法  齿轮磨损  预测
英文关键词: RBF neural network  Particle swarm optimization algorithm  Gear wear  Prediction
基金项目: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)
作者单位E-mail
王雅 新疆大学机械工程学院 452289145@qq.com 
孙耀宁 新疆大学机械工程学院  
李瑞国 新疆大学机械工程学院  
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中文摘要:
      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。
英文摘要:
      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 K727840ZW 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.
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