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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.


Training error results for both architectures of neural networks.
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Related In: Results  -  Collection


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fig5: Training error results for both architectures of neural networks.

Mentions: Figure 5 describes the comparing results betweenhigh-order MLP (it consists of 2 hidden layers) and FCNNalgorithms for 100 000 iterations. As noted, the average meansquare error (MSE) of FCNN is less than the MLP structure. Inother word, we can say that an average recognition


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

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

Training error results for both architectures of neural networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Training error results for both architectures of neural networks.
Mentions: Figure 5 describes the comparing results betweenhigh-order MLP (it consists of 2 hidden layers) and FCNNalgorithms for 100 000 iterations. As noted, the average meansquare error (MSE) of FCNN is less than the MLP structure. Inother word, we can say that an average recognition

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.