Advances in Signal Processing Vol. 1(2), pp. 19 - 32
DOI: 10.13189/asp.2013.010202
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Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E-nose Sensors


Pawel Plawiak1,2,*, Wojciech Maziarz3
1 Department of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering AGH University of Science and Technology 30 Mickiewicza Av., 30-059 Krakow, Poland
2 Faculty of Physics, Mathematics and Computer Science, Cracow University of Technology, 24 Warszawska st., F-5, 31-155
3 Department of Computer Science, Electronics and Telecommunications AGH University of Science and Technology 30 Mickiewicza Av., 30-059 Krakow, Poland

ABSTRACT

The data collected from electronic nose systems are multidimensional and usually contain a lot of redundant information. In order to extract only the relevant data, different computational techniques are developed. The article presents and compares selected pattern recognition algorithms in application to qualitative determination of different brands of tea. The measured responses of an array of 18 semiconductor gas sensors formed input vectors used for further analysis. The initial data processing consisted on standardization, principal component analysis, data normalization and reduction. Soft computing one can divide into single method systems using neural networks, fuzzy systems, and hybrid systems like evolutionary-neural, neuro-fuzzy, evolutionary-fuzzy. All the presented systems were evaluated based on accuracy (generated error) and complexity (number of parameters and training time) criteria. A novel method of forming input data vector by aggregation of the first three principal components is also presented.

KEYWORDS
Artificial intelligence methods, Soft computing, Computational intelligence, Neural networks, Genetic algorithms, Fuzzy systems, Pattern Recognition, Signal processing, PCA, Tea, E-nose, Chemometrics

Cite this paper
Pawel Plawiak , Wojciech Maziarz (2013). Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E-nose Sensors. Advances in Signal Processing, 1 , 19 - 32. doi: 10.13189/asp.2013.010202.