【摘要】 粮食产后储藏损耗是困扰粮食储藏企业的一大难题,也是影响企业经济效益的重要因素,因此对粮食储藏环节中损耗的评估,对于粮食产后减损具有重要的意义。本文通过调查问卷,对粮食储藏中影响损耗的因素进行调查,将获得的数据通过支持向量机(Support Vector Machine,SVM)模型进行建模,对储藏环节中的粮食损耗进行智能评估。同时,为了提高模型的精度,采用随机漂移粒子群优化(Random Drift Particle Swarm Optimization,RDPSO)算法对SVM的参数进行训练,充分利用RDPSO算法的全局搜索能力找到模型参数的最优解。实验结果表明运用RDPSO算法优化的SVM模型,能够得到比基本的SVM模型和线性回归模型更准确的粮食损耗预测。更多还原

【Abstract】 The storage loss of grain during post-harvest stages is a major problem that plagues grain storage enterprises,and thus also it is an important factor affecting the economic benefits of enterprises. Therefore,the assessment of the storage loss of grain is of great significance for post-harvest loss reduction of grain. This paper investigates the factors influencing storage loss of grain through questionnaires,and models the data by the Support Vector Machine( SVM) model to intelligently evaluate the grain loss in storage stage. Meanwhile,in order to improve the accuracy of the model,this paper uses Random Drift Particle Swarm Optimization( RDPSO) algorithm to train the parameters of SVM,by making full use of the strong global search ability of the RDPSO algorithm to find the optimal solution of the model parameters. The experimental results show that the SVM model optimized by RDPSO algorithm can obtain more accurate grain loss prediction than basic SVM model and linear regression model.更多还原