Computer Engineering Department, Engineering Faculty, Fatih University, 34500, Istanbul, Turkey.
Sensors (Basel). 2009;9(9):7308-19. doi: 10.3390/s90907308. Epub 2009 Sep 11.
Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.
电子鼻被开发为用于自动检测和分类气味、蒸气和气体的系统。人工神经网络 (ANN) 已被用于分析复杂数据和识别模式，并在电子鼻应用中识别挥发性化合物和气味方面显示出有前途的结果。当 ANN 与传感器阵列结合使用时，可检测到的化学物质的数量通常大于独特传感器类型的数量。由于其标准样式，即通过神经网络或多元分析识别具有部分重叠特异性的多个传感器的输出模式，因此应该将气味感测系统扩展到新的领域。本文描述了使用基于小脑模型关节控制器 (CMAC) 的神经网络开发的危险气味识别应用程序的设计、实现和性能评估。
