基于遗传RBF网络的电子鼻对苹果质量的评定

Quality Evaluation of Apples Using Electronic Nose Based on GA-RBF Network

提出一种根据苹果气味对苹果进行无损检测的方法 ,研制了一套适合苹果气味检测的电子鼻系统。对好坏苹果各 5 0个进行了检测 ,在获得传感器阵列数据的基础上 ,从每个传感器曲线中提取了 5个特征参数 ,作为模式识别的输入向量。用主成分分析法和遗传 RBF网络对所测的样本进行分析 ,主成分分析可较好的把好坏苹果区分开 ,遗传 RBF网络对训练集的回判正确率和对测试集的测试正确率分别为 10 0 %和 96 .4 %。

A new method to classify apples by the odor of apples is given, and an electronic nose equipment to classify apples is developed in this paper. Fifty good apples and fifty bad apples bought from the super-market are classed. Five feature parameters are developed from every data curve of sensor arrays, and all the feature parameters are called input vectors. Principal component analysis (PCA) and genetic algorithm radial based function neural network (GA-RBF) are used to combine the optimum feature parameters. Good separation among the gases of different apples is obtained using principal component analysis but a bit is overlapped. The recognition probability of the GA-RBF to the learning samples and the testing samples are 100% and 96.4%.