Applied Technologies and Innovations

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Volume 8
Issue 3
Online publication date 2012-11-15
Title Methods and algorithms of selection the informative attributes in systems of adaptive data processing for analysis and forecasting
Author Olimjan Djumanov
Abstract The principles, methods and algorithms of informative attributes selection were developed for optimization of description and representation for the objects in systems of adaptive data processing, where data are non-stationary by nature. The proposed algorithms of informative attributes selection for one-dimensional time series are based on the simplified ratings of correlation, mathematical expectation, dispersion of attributes. The algorithms have been developed using dynamic properties of information, i.e. control of conditional moments and parameters of regression model on confidential intervals. The methods and algorithms of adaptive data processing were tested on an example of extensive data represented for forecasting in systems of electro-supply.   
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References
Djumanov, O., 2008. “The system of image recognition for  dynamic processes objects on a basis of neural technologies”, 9th International Conference on Electronics, Information and Communication [9-ya Mezhdunarodnaya Konferenciya po Elektronike, Informacii i Kommunikacii], in Russian, Tashkent, pp.286-90 

Djumanov, O., 2011. “Adaptive designing for neuronetworking system of processing the data with non-stationary nature,” ATI - Applied Technologies & Innovations, Vol.4(1), pp.48-57 

Haykin, S., 1996. Adaptive Filter Theory, 3rd edition, Englewood Cliffs, NJ: Prentice-Hall

Sankar K. Pal, Sushmita, M., 1992. “Multilayer perceptron, fuzzy sets and classification,” IEEE Transactions on Neural Networks, Vol.3(5), pp.683-97

Umbaugh, S., 1998. “Computer vision and image processing,” Englewood Cliffs, NJ: Prentice Hall

Keywords Non-stationary object, continuous information, informative attributes, representation of object, statistical parameters, dynamic properties, system of adaptive data processing, approximation, intellectual analysis
DOI http://dx.doi.org/10.15208/ati.2012.16
Pages 45-55
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