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Sensors 2009, 9(9), 7308-7319; doi:10.3390/s90907308

Hazardous Odor Recognition by CMAC Based Neural Networks

1
Computer Engineering Department, Engineering Faculty, Fatih University, 34500, Istanbul, Turkey
2
Computer Engineering Department, Engineering Faculty, Haliç University, 34381, Istanbul, Turkey
*
Author to whom correspondence should be addressed.
Received: 1 July 2009 / Revised: 19 August 2009 / Accepted: 3 September 2009 / Published: 11 September 2009
(This article belongs to the Section Chemical Sensors)
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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. View Full-Text
Keywords: hazardous odors; electronic nose; CMAC neural networks; recognition hazardous odors; electronic nose; CMAC neural networks; recognition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Bucak, İ.Ö.; Karlık, B. Hazardous Odor Recognition by CMAC Based Neural Networks. Sensors 2009, 9, 7308-7319.

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