The Most Probable Path Derivation for Quasi-Birth–Death Processes by Using Symbolic Control

Yıl 2026, Cilt: 9 Sayı: 1, 238 - 253, 14.01.2026

Mete Özbaltan

https://doi.org/10.47495/okufbed.1653681

Öz

In this study, we utilize the control theory of discrete event systems to compute the most likely paths in quasi-birth-death processes through hidden Markov models. We begin by discussing how quasi-birth-death processes can be framed as a control problem within the context of discrete event systems. Following this, we encode quasi-birth-death processes as a controller synthesis problem, explaining how to apply safety and optimization algorithms enables the controller to select the best paths. We validate our proposed method through several case studies and report its superior performance compared to various state-of-the-art techniques found in the literature.

Anahtar Kelimeler

Discrete Event Systems , Discrete Controller Synthesis , Quasi-Birth–Death Processes , Hidden Markov Models , Maximum Likelihood Estimation , Optimization Algorithm

Sembolik Kontrol Kullanarak Quasi-Doğum-Ölüm Süreçleri İçin En Olası Yol Hesaplaması

Öz

Bu çalışmada, gizli Markov modelleri aracılığıyla quasi-doğum-ölüm süreçlerinde en olası yolları hesaplamak için ayrık olay sistemlerinin kontrol teorisini kullanıyoruz. İlk olarak, quasi-doğum-ölüm süreçlerinin, ayrık olay sistemleri ağlamında bir kontrol problemi olarak nasıl çerçevelenebileceğini ele alıyoruz. Ardından, quasi-doğum-ölüm süreçlerini bir kontrol sentez problemi olarak kodluyoruz. Güvenlik ve optimizasyon algoritmalarının uygulanması ile elde edilen denetleyicinin en iyi yolları nasıl seçmesini sağladığını açıklıyoruz. Önerilen yöntemi vaka çalışmaları ile doğruluyoruz ve sunduğumuz yaklaşımın literatürde kullanılan yöntemlere kıyasla üstün performans sağladığını gösteriyoruz.

Anahtar Kelimeler

Ayrık Olay Sistemleri , Ayrık Denetleyici Sentezi , Quasi-Doğum-Ölüm Süreçleri , Gizli Markov Modelleri , Maksimum Olasılık Tahmini , Optimizasyon Algoritması

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