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 本文首先分析了影響脫粒裝置性能的各個因素以及傳統(tǒng)研究脫粒性能的各種方法如單因素法、正交試驗法、方差分析法以及回歸分析法的缺陷,在此基礎上提出了采用bp神經(jīng)網(wǎng)絡對脫粒裝置性能模型進行優(yōu)化,采用這種方法優(yōu)化脫粒裝置性能模型可以更好地刻劃脫粒裝置所具有的多輸入多輸出、復雜非線性以及不確定性等特征。