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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.


The recognition form.
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Related In: Results  -  Collection


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fig4: The recognition form.

Mentions: This system allows users to obtain the desired datafrom a particular odorant (perfume). There are two ways to obtaindata by using a handheld odor meter. These are real-time samplingdata and memory sampling data. The sensor output voltages (rawdata) were sampled approximately every one second. The last formis ANN System, which classifies the training and test data of odorsamples (see Figure 4). The numberof features in each input pattern, in our case, is 16 × 20 (each odorcontains 20 samples). The numbers of output units are 16 outputsfor 16 different classes of odor samples.


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

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

The recognition form.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: The recognition form.
Mentions: This system allows users to obtain the desired datafrom a particular odorant (perfume). There are two ways to obtaindata by using a handheld odor meter. These are real-time samplingdata and memory sampling data. The sensor output voltages (rawdata) were sampled approximately every one second. The last formis ANN System, which classifies the training and test data of odorsamples (see Figure 4). The numberof features in each input pattern, in our case, is 16 × 20 (each odorcontains 20 samples). The numbers of output units are 16 outputsfor 16 different classes of odor samples.

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.