An adaptive weak estimation method based on stochastic learning

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye

Tezin Onay Tarihi: 2021

Tezin Dili: İngilizce

Öğrenci: AHMET KUTALMIŞ COŞKUN

Danışman: Mustafa Borahan Tümer

Özet:

Dynamic systems are highly complex and hard to deal with due to their subject-

and time-varying nature. The fact that most of the real world systems/events are

of dynamic character makes modeling and analysis of such systems inevitable and

charmingly useful. Working with such systems require the learning algorithm to be

able to adapt to new conditions that might occur at unknown times.

One promising estimation method that is capable of unlearning past information

to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by

Oommen and Rueda (2006). This method maintains an always up-to-date estimate of

the target parameter by employing a multiplicative update rule that adjusts previous

estimate based on the current observation. However, due to using a constant learning

rate, it faces a trade-off between plasticity and stability, which is also referred to as

exploration versus exploitation dilemma in some contexts.

In this study, considering the problem of estimating parameters that characterize a

statistical distribution that involves abrupt changes, SLWE is modeled as a random

walk and asymptotic behavior of estimates is theoretically analyzed to obtain a

statistical model. Utilizing this model, changes in stationarity is detected to switch

between exploratory and exploitative learning modes. Experimental evaluations on

both synthetic and real world data show that the proposed method tracks the target

parameter with 15% to 50% less error.