Neural network Boolean factor analysis and application

Authors:
Dusan Husek;Alexander Frolov;Pavel Polyakov;Vaclav Snasel
Affiliations:
ICS AS CR, v.v.i., Prague 8, Czech Republic;Institute of Higher, Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia;Institute of Optical Neural Technologies, Russian AS, Moscow, Russia;VSB TU Ostrava, Departmet of Computer Science, Ostrava, Czech Republic
Venue:
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2007

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Abstract

The recurrent Neural network capable to provide the Boolean factor analysis of the binary data sets of high dimension and complexity is applied to roll-call voting problem. The method of sequential factor extraction, based on the Lyapunov function is discussed in deep. Efficiency of this attempt is shown on simulated data and on real data from Russian parliament as well.