摘要
棉花是新疆的支柱产业,棉花质量流量的在线检测对于提高生产效率,节能降耗具有十分积极的意义。本研究提出一种基于神经网络的棉花质量流量预测模型,训练结果表明:预测值与试验结果具有较好的一致性,避免了机理分析和推导过程,具有简易性和普遍适用性的优点。
Cotton is the pillar industry of Xinjiang, the online measurement of cotton quality and flow rate is of importance to improving production efficiency, saving energy and reducing consumption. This paper designed a prediction model of cotton quality and flow rate based on neural networks. The results showed that the predicted values and the test results have good consistency, while avoiding the mechanism analysis and derivation. It also has the advantages of simplicity and general applicability.
出处
《农业网络信息》
2013年第12期16-19,共4页
Agriculture Network Information
基金
国家自然基金项目"棉花质量流量光学测量模型和试验研究"(编号:61164002)
石河子大学优秀青年项目"基于神经网络的气固两相流量软测量模型的研究"(编号:2012ZRKXYQ-YD04)
关键词
棉花流量测量
神经网络
软测量
训练
cotton flow measurement
neural networks
soft sensor
training
