Modular Neural Network Control of Nonlinear Systems

Year 2023, , 625 - 635, 01.09.2023

Şerafetdin Baloğlu , Muammer Gökbulut

https://doi.org/10.35234/fumbd.1289724

Abstract

A neural network is modular (MNN) if the computation performed by an artificial neural network (ANN) can be decomposed into two or more modules (subsystems) operating on the input space without communicating with each other. Modularity is a manifestation of the divide-and-conquer principle, which allows a solution by dividing a complex computational task into simpler tasks, combining individual solutions of modules that tend to learn and specialize in different regions of the input space. In this study, the modeling of two nonlinear systems with MCA and the audit successes were examined and the results obtained were compared with ANN. When the comparison results made in the modeling and inspection of the systems are examined, it has been determined that the MYSA performance is better than the ANN.

Keywords

Module neural networks, nonlinear systems, modeling and control

Modül Yapay Sinir Ağları ile Doğrusal Olmayan Sistemlerin Denetimi

Abstract

Yapay sinir ağı (YSA) tarafından gerçekleştirilen hesaplama, birbiriyle iletişim kurmadan girdi uzayı üzerinde çalışan iki veya daha fazla modüle (alt sistemler) ayrıştırılabiliyorsa, sinir ağı modülerdir (MYSA). Modülerlik, karmaşık bir hesaplama görevini daha basit görevlere bölerek girdi uzayının farklı bölgelerini öğrenip uzmanlaşma eğilimindeki modüllerin bireysel çözümlerini birleştirme yaparak çözüme izin veren böl ve fethet ilkesinin bir tezahürüdür. Bu çalışmada, doğrusal olmayan iki sistemin MYSA ile modellenmesi ve denetim başarıları incelenerek elde edilen sonuçlar YSA ile karşılaştırılmıştır. Sistemlerin modelleme ve denetiminde yapılan karşılaştırma sonuçlarına bakıldığında MYSA performansının YSA’ ya göre iyi olduğu tespit edilmiştir.

Keywords

Modül yapay sinir ağları, doğrusal olmayan sistemler, modelleme ve denetim

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