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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.


Error according to number of nodes for one hidden layer of FCNN.
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Related In: Results  -  Collection


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fig6: Error according to number of nodes for one hidden layer of FCNN.

Mentions: It can be seen in Figure 6, the number of hiddenlayers was fixed to one hidden layer for ANN structures, and thenumber of nodes (or units) in that hidden layer was changedseveral times. Also, the iteration number was fixed to 100 000iterations. These results (of the output error) were drawntogether with the number of nodes in the hidden layer in acurve.


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

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

Error according to number of nodes for one hidden layer of FCNN.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Error according to number of nodes for one hidden layer of FCNN.
Mentions: It can be seen in Figure 6, the number of hiddenlayers was fixed to one hidden layer for ANN structures, and thenumber of nodes (or units) in that hidden layer was changedseveral times. Also, the iteration number was fixed to 100 000iterations. These results (of the output error) were drawntogether with the number of nodes in the hidden layer in acurve.

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.