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

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.

Anahtar Kelimeler:

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

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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