An adaptive estimation method with exploration and exploitation modes for non-stationary environments - TÜMER, MUSTAFA BORAHAN (My University, 2022)
Dynamic systems are highly complex and hard to deal with due to their subject-and time-varying na-ture. 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. One promising estimation method that is ca-pable of unlearning past information to deal with non-stationarity is Stochastic Learning Weak Estimator (SLWE) by Oommen and Rueda (2006). However, due to using a constant learning rate, it faces a trade-off between plasticity and stability. In this paper, we model SLWE as a random walk and provide rigorous theoretical analysis of asymptotic behavior of estimates to obtain a statistical model. Utilizing this model, we detect changes in stationarity to switch between exploratory and exploitative learning modes. Exper-imental evaluations on both synthetic and real world data show that the proposed method outperforms related algorithms in different types of drifts. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords: Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Sinyal İşleme; Bilgisayar Bilimleri; Algoritmalar; Mühendislik ve Teknoloji; Information Systems, Communication and Control Engineering; Signal Processing; Computer Sciences; algorithms; Engineering and Technology; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; Bilgisayar Bilimi; Mühendislik, Bilişim ve Teknoloji (ENG); MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK; Mühendislik; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; Engineering, Computing & Technology (ENG); ENGINEERING, ELECTRICAL & ELECTRONIC; ENGINEERING; Genel Mühendislik; Yapay Zeka; Genel Bilgisayar Bilimi; Mühendislik (çeşitli); Elektrik ve Elektronik Mühendisliği; Bilgisayar Bilimi (çeşitli); Bilgisayarla Görme ve Örüntü Tanıma; Bilgisayar Bilimi Uygulamaları; Fizik Bilimleri; General Engineering; Artificial Intelligence; General Computer Science; Engineering (miscellaneous); Electrical and Electronic Engineering; Computer Science (miscellaneous); Computer Vision and Pattern Recognition; Computer Science Applications; Physical Sciences; Stochastic learning; Concept drift; Change detection; Parameter estimation; Dynamic learning rate; PATTERN-RECOGNITION; WEAK ESTIMATION; PARAMETER; ONLINE; MOTION; DRIFT