The main testing parameters include : productivity , weight of specimen , electric power , the temperature of bearings and the noise from machinery etc . the main controlling parameters include : auto - taking of original seed , auto - controlling of two - directed valve , adjusting of productivity and automatic starting / stopping system for each equipment 其主要檢測量有:生產(chǎn)率、接樣重量、耗電量、軸承溫升、機器噪聲等;主要控制量為:原始物料的自動接料、接樣三通的自動控制、喂入量調(diào)節(jié)、實驗臺各環(huán)節(jié)的自動開機關機等。
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 脫粒裝置的工作過程極為復雜,影響脫粒性能的因素很多,除了喂入量、滾筒轉速、凹板長度與入口間隙等確定性因素外,還有作物品種、作物含水率以及谷草比等一些不確定性因素,因此脫粒裝置是一個具有不確定性、多輸入多輸出的復雜非線性系統(tǒng)。
It holds that the improvement of the detectable and cleaning rate of foreign matter can be done by improving none - colored foreign matter detecting , raising the opening rate of the feeding lattice , well controlling the cotton feeding speed , enhancing the lighting source and sensor ' s resolution , use of multi - sensors as well as the synchro detecting and cleaning 提高異纖的檢出率應從改善無色異纖檢測、提高喂入筵棉開松度、控制棉流速度、增加光源亮度和提高傳感器分辨率、采用多組傳感器以及盡可能使檢測和噴除同步進行等方面采取綜合措施。
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語言開發(fā)的模型仿真軟件實現(xiàn)了基于ga - bp算法的神經(jīng)網(wǎng)絡用于對脫粒裝置性能模型進行優(yōu)化的的思想,并利用該模型仿真軟件對洛陽工學院農(nóng)機研究室1994年的小麥控速喂入脫粒裝置進行了性能建模仿真試驗,試驗結果驗證了該模型用于脫粒裝置性能建模研究的可行性。