Kermani B G, Schiffman S S, Nagle H T
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh 27695-7911, USA.
IEEE Trans Biomed Eng. 1999 Apr;46(4):429-39. doi: 10.1109/10.752940.
Sensitivity, repeatability, and discernment are three major issues in any classification problem. In this study, an electronic nose with an array of 32 sensors was used to classify a range of odorous substances. The collective time response of the sensor array was first partitioned into four time segments, using four smooth time-windowing functions. The dimension of the data associated with each time segment was then reduced by applying the Karhunen-Loéve (truncated) expansion (KLE). An ensemble of the reduced data patterns was then used to train a neural network (NN) using the Levenberg-Marquardt (LM) learning method. A genetic algorithm (GA)-based evolutionary computation method was used to devise the appropriate NN training parameters, as well as the effective database partitions/features. Finally, it was shown that a GA-supervised NN system (GANN) outperforms the NN-only classifier, for the classes of the odorants investigated in this study (fragrances, hog farm air, and soft beverages).
敏感性、可重复性和辨别力是任何分类问题中的三个主要问题。在本研究中,使用具有32个传感器阵列的电子鼻对一系列有气味物质进行分类。首先,使用四个平滑时间窗函数将传感器阵列的集体时间响应划分为四个时间段。然后,通过应用卡尔胡宁-勒夫(截断)展开(KLE)来降低与每个时间段相关的数据维度。接着,使用简化后的数据模式集合,采用列文伯格-马夸尔特(LM)学习方法训练神经网络(NN)。基于遗传算法(GA)的进化计算方法用于设计合适的NN训练参数以及有效的数据库分区/特征。最后,结果表明,对于本研究中所研究的气味剂类别(香料、养猪场空气和软饮料),GA监督的NN系统(GANN)优于仅使用NN的分类器。