ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

基于改進(jìn)粒子群與神經(jīng)網(wǎng)絡(luò)的機(jī)械結(jié)合面法向剛度建模
CSTR:
作者:
作者單位:

作者簡介:

通訊作者:

中圖分類號:

基金項(xiàng)目:

國家重大科技專項(xiàng)資助項(xiàng)目（2009ZX04014-32）、國家重點(diǎn)基礎(chǔ)研究發(fā)展計劃(973計劃)資助項(xiàng)目（2009CB724406）和陜西省科學(xué)研究計劃資助項(xiàng)目（09JK669）


Modeling of Machined Joints Normal Stiffness Using Modified PSO-BP Neural Network Algorithm
Author:
Affiliation:

Fund Project:

  • 摘要
    • |
  • 圖/表
    • |
  • 訪問統(tǒng)計
    • |
  • 參考文獻(xiàn)
    • |
  • 相似文獻(xiàn)
    • |
  • 引證文獻(xiàn)
    • |
  • 資源附件
    • |
  • 文章評論
    摘要:

    為了提高機(jī)械結(jié)合面法向接觸剛度預(yù)測精度,，提出一種改進(jìn)粒子群優(yōu)化算法,，并用改進(jìn)粒子群算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的參數(shù)組合,，實(shí)現(xiàn)了粒子群和BP神經(jīng)網(wǎng)絡(luò)相結(jié)合的算法模型,。將影響結(jié)合面法向接觸剛度的因素進(jìn)行了特征分析和定量化描述,，并用該算法進(jìn)行法向接觸剛度預(yù)測和相對誤差分析,。計算結(jié)果表明,，計算準(zhǔn)確度可達(dá)92%，實(shí)現(xiàn)了多種影響因素組合下的機(jī)械結(jié)合面法向接觸剛度的建模,。

    Abstract:

    With the aim to improve forecasting accuracy of the normal contact stiffness of machined joints, the modified particle swarm optimizer (MPSO) algorithm was proposed. The BP neural network parameters were optimized by the MPSO algorithm. The normal contact stiffness of machined joints was forecasted under different experimental conditions, and the relative errors were analyzed. The results showed that the forecast precision could reach to 92%, and the contact stiffness of machined joints was modeled for various affecting factors. 

    參考文獻(xiàn)
    相似文獻(xiàn)
    引證文獻(xiàn)
引用本文

楊紅平,傅衛(wèi)平,師彪,王雯,楊世強(qiáng),王偉.基于改進(jìn)粒子群與神經(jīng)網(wǎng)絡(luò)的機(jī)械結(jié)合面法向剛度建模[J].農(nóng)業(yè)機(jī)械學(xué)報,2011,42(3):219-223,233. Yang Hongping,Fu Weiping, Shi Biao, Wang Wen, Yang Shiqiang, Wang Wei. Modeling of Machined Joints Normal Stiffness Using Modified PSO-BP Neural Network Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(3):219-223,233.

復(fù)制
分享
文章指標(biāo)
  • 點(diǎn)擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期:
  • 出版日期:
文章二維碼