基于RBF神经网络的叶丝生丝含水率预测

Moisture Content of Raw Cut Tobacco Forecasting Based on RBF Neural Network

叶丝生丝含水率决定烟丝的感官质量及内在品质,是烟丝加工过程中的一项重要指标。介绍RBF神经网络的基本原理和算法;给出建立RBF神经网络模型的具体过程;并将模型应用于预测叶丝生丝含水率。预测结果与实际值误差小于3%,表明了该模型预测叶丝生丝含水率的可行性和有效性。

Moisture content is an important index in tobacco processing, which determines the sensory and internal quality of cut tobacco. This paper introduces the basic principle and algorithm of RBF neural network, also gives the specific process of establishing RBF neural network model, and applied the method to forecast the moisture content. The error between the predicted and actual values is less than 3%. It indicates that the model is feasible and effective for moisture content forecast.