YAPAY ARI KOLONİSİ ALGORİTMASI İLE OPTİMİZE EDİLEN HAMMERSTEIN MODEL KULLANARAK SİSTEMLERİN KİMLİKLENDİRİLMESİ

Year 2018, Volume: 7 Issue: 1, 83 - 98, 31.01.2018

Hasan Zorlu , Selçuk Mete Şaban Özer

https://doi.org/10.28948/ngumuh.386351

Cited By: 1

Abstract

   Hammerstein model, doğrusal olmayan alt
model çıkışının doğrusal olan bir alt modelin girişine seri bağlanması ile
oluşan bir blok model yapısıdır. Literatürde, Hammerstein modellerde çoğunlukla
doğrusal olmayan bölümler için doğrusal olmayan hafızasız polinom (MPN - memoryless polynomial nonlinear) model
ve doğrusal bölümler için sonlu darbe cevaplı (FIR- finite impulse response) ya da sonsuz darbe cevaplı (IIR- infinite impulse response) model tercih edilmektedir. Literatürden farklı
olarak bu çalışmada doğrusal olmayan bölüm için MPN yerine ikinci derece
volterra (SOV -
Second Order Volterra)
model tercih edilmiştir. Bu açıdan doğrusal olmayan SOV ve doğrusal FIR modelin
kaskat bağlanmasından oluşan yeni bir Hammerstein model sunulmuştur.
Simulasyonlarda, yapay arı kolonisi (ABC- artificial bee colony) algoritmasıyla optimize edilen Hammerstein model ile
farklı sistemler kimliklendirilmiştir. Simulasyon sonuçlarında ABC algoritması
ile önerilen modelin etkili ve güçlü olduğu görülmüştür.

Keywords

Sistem kimliklendirme, Hammerstein model, yapay arı koloni algoritması, klonal seçim algoritması, yenilemeli en küçük kareler algoritması

SYSTEM IDENTIFICATION USING HAMMERSTEIN MODEL OPTIMIZED WITH ARTIFICIAL BEE COLONY ALGORITHM

Abstract

   Hammerstein model is formed
by cascade of linear and nonlinear parts. In literature, memoryless polynomial
nonlinear (MPN) model for nonlinear part and finite impulse response (FIR)
model or infinite impulse response (IIR) model for linear part are mostly
preferred for Hammerstein models. This paper different from the studies in
literature, focuses on the success of Hammerstein block model that Second Order
Volterra (SOV) is preferred instead of MPN as nonlinear part. In this context, a new Hammerstein model is presented which is obtained by
cascade form of a nonlinear SOV and a linear FIR model.
In simulations, different types of system are identified by proposed
Hammerstein model which is optimized with ABC (artificial bee colony)
algorithm. The simulation results reveal effectiveness and robustness of the
proposed model with ABC algorithm.

Keywords

System identification, Hammerstein model, artificial bee colony algorithm, clonal selection algorithm, recursive least square algorithm

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