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Fuzzy clustering neural networks for real-time odor recognition system.

Karlık B, Yüksek K - J Autom Methods Manag Chem (2007)

Bottom Line: In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly.Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system.Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

View Article: PubMed Central - PubMed

Affiliation: Computer Engineering Department, Faculty of Engineering, Fatih University, 34500 Istanbul, Turkey.

ABSTRACT
The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

No MeSH data available.


Sensor configuration and measurement principle.
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Related In: Results  -  Collection


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fig2: Sensor configuration and measurement principle.

Mentions: In this study a “handheld odor meter, OMX-GR” isused to obtain odor data. This is completely manufactured by FiSas an OEM product. The OMX-GR sensor indicates two factors ofodor, “strength” and “classification”, with numeric values. This is very useful for various applications related to odordetection and measurement. Also, real-time continuous data can bestored into a personal computer through RS-232C interface. As itcan be seen in Figure 2, thestrength and classification of odor can be identified by using twodifferent gas sensors: one has a specific sensitivity to a lightand fresh smell and the other has a specific sensitivity to aheavy and bad smell. Memory sampling of this odor meter issuitable to store 16 different patterns of odor sampling.


Fuzzy clustering neural networks for real-time odor recognition system.

Karlık B, Yüksek K - J Autom Methods Manag Chem (2007)

Sensor configuration and measurement principle.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2267212&req=5

fig2: Sensor configuration and measurement principle.
Mentions: In this study a “handheld odor meter, OMX-GR” isused to obtain odor data. This is completely manufactured by FiSas an OEM product. The OMX-GR sensor indicates two factors ofodor, “strength” and “classification”, with numeric values. This is very useful for various applications related to odordetection and measurement. Also, real-time continuous data can bestored into a personal computer through RS-232C interface. As itcan be seen in Figure 2, thestrength and classification of odor can be identified by using twodifferent gas sensors: one has a specific sensitivity to a lightand fresh smell and the other has a specific sensitivity to aheavy and bad smell. Memory sampling of this odor meter issuitable to store 16 different patterns of odor sampling.

Bottom Line: In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly.Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system.Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

View Article: PubMed Central - PubMed

Affiliation: Computer Engineering Department, Faculty of Engineering, Fatih University, 34500 Istanbul, Turkey.

ABSTRACT
The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

No MeSH data available.