基于烟草中致香成分的人工神经网络分类模型

A Neural Network Recognition Model Based on Aroma Components in Tobacco

从烟叶化学成分和结合评吸结果确定的烟叶品质的关系入手,用遗传BP神经网络建立了基于烟草中致香物质含量的烟草模式识别模型.分析结果表明,使用该方法能全局优化基于烟草致香成分的神经网络结构,较之梯度下降法的神经网络的训练效率更高,得到的识别结果也更加准确.它可以作为一种新的判别烟草香味特征的识别手段.

A pattern recognition model for tobacco quality was established by using back- propagation （BP） neural network combined with genetic algorithm, based on the relationship between tobacco quality with expert experience and aroma chemical component in tobacco. Experiment results showed that the neural network structure of tobacco aroma components was global optimized, and the neural network trained by genetic algorithm was more efficient and correct than by gradient descent method. It can be utilized as a new recognition method for identifving the aromatic characteristics of tobacco.