Construction of a learning automaton for cycle detection in noisy data sequences

Authors:
Aleksei Ustimov;Borahan Tümer
Affiliations:
Faculty of Engineering, Marmara University, Kadiköy, İstanbul, Turkey;Faculty of Engineering, Marmara University, Kadiköy, İstanbul, Turkey
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

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Abstract

This paper investigates the problem of cycle detection in periodic noisy data sequences. Our approach is based on reinforcement learning principles. A constructive approach is used to devise a variable structure learning automaton (VSLA) that becomes capable of recognizing the potential cycles of the noisy input sequence. The constructive approach allows for VSLAs to analyze sequences not requiring a priori information about their cycle and noise. Consecutive tokens of the input sequence are presented to VSLA, one at a time, where VSLA uses data’s syntactic property to construct itself from a single state at the beginning to a topology that is able to recognize an unknown cycle of the given data. The main strength of this approach is applicability in many fields and high recognition rates.