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脱粒装置的英文

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"脱粒装置"怎么读用"脱粒装置"造句

英文翻译手机手机版

  • seed husking establishment
  • sheller unit
  • threshing mechanism
  • threshing unit

例句与用法

  • Threshing machinery and threshing equipment
    脱粒机械与脱粒装置
  • At the same time , we give a neural network ' s topological structure , which will use to solve the concrete problem oriented to threshing performance modeling
    同时针对脱粒装置性能建模这一具体问题,分析给出了用于对脱粒性能进行建模的基于ga - bp算法神经网络的实现。
  • Seed - husking plant is the key unit of combine , whose improvement and perfection is mainly based on the nonlinear mathematical model of the threshing performance with its influencing factors
    脱粒装置是联合收获机械的核心部件,它的改进与完善是以脱粒性能与其影响因素之间的非线性模型为基础的。
  • Under the uncertain conditions , the purpose of setting up nonlinear mathematical model of the threshing performance with its influencing factors is to optimize all parameters of construction and motion so that we can get the optimum threshing performances
    建立脱粒装置性能与其影响因素之间非线性模型的目的就是在不确定环境下,对影响脱粒性能的各因素进行优化使脱粒装置的各个性能指标达到最佳要求。
  • In the process of threshing , the significance of factors , which influence threshing performance , not only include some certain factors such as feed quantity , cylinder rotary speed , distance between cylinder and concave , thresher concave radian but also include some uncertain factors such as the crop ' s variety , water content and the proportion of cereal straw . for this reason , the seed - husking plant is system with a character of uncertain , multi - input - output and complex nonlinear
    脱粒装置的工作过程极为复杂,影响脱粒性能的因素很多,除了喂入量、滚筒转速、凹板长度与入口间隙等确定性因素外,还有作物品种、作物含水率以及谷草比等一些不确定性因素,因此脱粒装置是一个具有不确定性、多输入多输出的复杂非线性系统。
  • In this paper , we first analyze each factor of influencing threshing performance , and deficiency of all traditional methods such as single factor , orthogonal experiment , variance analysis and regression analysis , which have been used to study the threshing performance . in the basis of above analysis , we propose a new method of threshing performance modeling - a bp neural network . by use the new ways of threshing performance modeling - a bp neural network , we can obtain the optimum model of threshing performance , which can better describe the seed - husking plant ' s feature of complex nonlinear , multi - input - output and indefinite
    本文首先分析了影响脱粒装置性能的各个因素以及传统研究脱粒性能的各种方法如单因素法、正交试验法、方差分析法以及回归分析法的缺陷,在此基础上提出了采用bp神经网络对脱粒装置性能模型进行优化,采用这种方法优化脱粒装置性能模型可以更好地刻划脱粒装置所具有的多输入多输出、复杂非线性以及不确定性等特征。
  • We used delphi language to develop a ga - bp neural network ' s simulation software in this paper , which implemented the thoughts of threshing performance modeling . using this simulation software , we gave a experiment on the speed - controlled threshing unit for wheat offered by the college engineering of luoyang , the result of test verified the feasibility of threshing performance modeling . in the end of this thesis , the application prospect and further research domains of ga - bp neural network are presented
    本文用delphi语言开发的模型仿真软件实现了基于ga - bp算法的神经网络用于对脱粒装置性能模型进行优化的的思想,并利用该模型仿真软件对洛阳工学院农机研究室1994年的小麦控速喂入脱粒装置进行了性能建模仿真试验,试验结果验证了该模型用于脱粒装置性能建模研究的可行性。
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