Hazardous Odor Recognition by CMAC Based Neural Networks

Sensors

İhsan Ömür Bucak¹ 

[1] Computer Engineering Department, Engineering Faculty, Fatih University, 34500, Istanbul, Turkey

关键词: hazardous odors;    electronic nose;    CMAC neural networks;    recognition;   

DOI  :  10.3390/s90907308

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland