摘要
利用13只气体传感器、1只温度传感器和1只湿度传感器构成的初始测试系统,对3种不同品种的食醋进行测试。运用小波包的三尺度分解来提取测试结果的分析特征向量,并借助主成分分析(PCA)技术,对传感器阵列进行了优化,得到由5只传感器构成的优化阵列。利用RBF神经网络,对原阵列和优化后阵列的鉴别效果进行比较,结果表明:优化后传感器阵列能将3种不同品种的食醋成功区分开,并且鉴别效果也得到明显改善。
The initial testing system made up of thirteen gas sensors, one temperature sensor and one humidity sensor is used to test three kinds of vinegars. Three-scale decomposition of the wavelet packet is employed to extract the feature values of measuring results, then principal component analysis （PCA） is applied to optimize sensor array. The optimized sensor array composed of five sensors is obtained finally. Using radial basis function neural network （RBFNN） to compare the identification effect between optimiz sensor array and original sensor array, the results of classification present that the optimized array can discriminate successfully three kinds of vinegars and improve the recognition performance compared with original array.
出处
《传感器与微系统》
CSCD
北大核心
2008年第8期75-77,86,共4页
Transducer and Microsystem Technologies
基金
河南省杰出青年科学基金资助项目(0612000400)
关键词
传感器阵列
食醋
小波包
主成分分析
神经网络
sensor array
vinegar
wavelet packet
principal component analysis （PCA）
neural network
