A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

Year 2019, Volume: 12 Issue: 1, 1 - 8, 31.01.2019

Selçuk Mete Hasan Zorlu Şaban Özer

https://doi.org/10.17671/gazibtd.459399

Cited By: 3

Abstract

This
paper aims to improve a new hybrid model for system identification area. The
proposed hybrid model consists of an adaptive Hammerstein model, an adaptive
Wiener model, and a Neuro-Fuzzy (NF) network based soft-switching mechanism
(SSM). SSM structure in hybrid model increases the success of block model by
selecting the best results of Hammerstein and Wiener model outputs. In
literature, there are various studies about NF based on Hammerstein or Wiener
model types applied to system identification. In the proposed model,
Hammerstein and Wiener models with NF network are used together different from
the literature. In simulation studies, five different type of systems are identified
with different models (Hammerstein, Wiener and the proposed hybrid model)
optimized by Recursive Least Square (RLS). Then the performances of these
models are compared. Simulation results reveal the effectiveness and robustness
of the proposed identification model. 

Keywords

Fuzzy neural networks, hybrid intelligent systems, optimization, soft switching, system identification

Sistem Kimliklendirme İçin Bulanık Sinir Ağı Esnek Anahtarlama Mekanizması Temelli Yeni Bir Karma Model

Abstract

Bu çalışmanın amacı sistem kimliklendirme
alanında yeni bir karma model geliştirmektir. Önerilen karma model
uyarlanabilen bir Hammerstein model, bir Wiener model ve esnek anahtarlama
mekanizmasına dayanan bulanık sinir ağını içermektedir. Karma modeldeki esnek
anahtarlama mekanizması Hammerstein ve Wiener model çıkışlarının en iyi
sonuçlarını seçerek blok model başarısını arttırmaktadır. Literatürde, sistem
kimliklendirmede uygulanan bulanık sinir ağı temelli Hammerstein ya da Wiener
modellerle ilgili birçok çalışma vardır. Önerilen modelde, bulanık sinir ağıyla
birlikte Hammerstein ve Wiener modelleri literatürden farklı olarak bir arada
kullanılmıştır. Simülasyon çalışmalarında, farklı tipteki beş sistem tekrarlayan
en küçük kare ile optimize edilmiş olan farklı modeller (Hammerstein, Wiener ve
önerilen model) ile kimliklendirilmiştir. Daha sonra bu modellerin
performansları karşılaştırılmıştır. Simülasyon çalışmaları önerilen modelin
etkinliğini ve sağlamlığını ortaya koymaktadır.

Keywords

Bulanık sinir ağı, karma zeki sistemler, optimizasyon, esnek anahtarlama, sistem kimliklendirme

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