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Hazardous Odor Recognition by CMAC Based Neural Networks
Computer Engineering Department, Engineering Faculty, Fatih University, 34500, Istanbul, Turkey
Computer Engineering Department, Engineering Faculty, Haliç University, 34381, Istanbul, Turkey
* Author to whom correspondence should be addressed.
Received: 1 July 2009; in revised form: 19 August 2009 / Accepted: 3 September 2009 / Published: 11 September 2009
Abstract: 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.
Keywords: hazardous odors; electronic nose; CMAC neural networks; recognition
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Cite This Article
MDPI and ACS Style
Bucak, İ.Ö.; Karlık, B. Hazardous Odor Recognition by CMAC Based Neural Networks. Sensors 2009, 9, 7308-7319.
Bucak İÖ, Karlık B. Hazardous Odor Recognition by CMAC Based Neural Networks. Sensors. 2009; 9(9):7308-7319.
Bucak, İhsan Ö.; Karlık, Bekir. 2009. "Hazardous Odor Recognition by CMAC Based Neural Networks." Sensors 9, no. 9: 7308-7319.