Adaptive Volterra filtering with complete lattice orthogonalization

Ozden, Mehmet Tahir; Kayran, A.H.; Panayırcı, Erdal

doi:10.1109/78.533732

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Do÷rusal olmayan sistemlerin kimliklendirilmesinde, güç serisine dayalÕ modelleme yaklaúÕmÕ, birçok sistemin davranÕúÕnÕ modellemekte kullanÕlÕr. Volterra, bilinear ve Polynomial Autoregressive (PAR) modelleri güç serisine dayalÕ do÷rusal olmayan modellerdir [2,3]. ÇÕkÕú iúareti, Volterra modelde [4], giriú iúaretinin a÷ÕrlÕklandÕrÕlmasÕ sonucu, PAR modelde [5] ise çÕkÕú iúaretinin a÷ÕrlÕklandÕrÕlmasÕ sonucu elde edilir.…”

Section: Giriúunclassified

Identification of nonlinear systems using Clonal Selection algorithm

Zorlu

Özer

2009

2009 IEEE 17th Signal Processing and Communications Applications Conference

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Özetçe Bu çalÕúmada, do÷rusal olmayan sistemlerin Klonal Seçme algoritmasÕ (KSA) ile uyarlanÕr olarak kimliklendirilmesi gerçekleútirilmiú olup Genetik algoritma (GA) ile baúarÕmÕ karúÕlaútÕrÕlmÕútÕr. Benzetim çalÕúmalarÕnda literatürde kÕyaslama problemlerinde sÕklÕkla kullanÕlan do÷rusal olmayan Box-Jenkins sistemi ile parametrik bir do÷rusal olmayan bilinear sistem bu algoritmalar yardÕmÕyla kimliklendirilmiútir. Benzetim sonuçlarÕna göre do÷rusal olmayan sistemlerin KSA algoritmasÕ kullanÕlarak düúük modelleme hatasÕ ile kimliklendirilebilece÷i görülmüútür. AbstractIn this work, Clonal Selection algorithm (CSA) has been applied to adaptive identification of nonlinear systems and compared its performance to that of Genetic algorithm (GA). Nonlinear Box-Jenkins system which is frequently used as a benchmark example for testing in literature and a parametric nonlinear bilinear system have been identified using these algorithms. The simulation results have shown that nonlinear systems can be identified using CSA with low modeling error.978-1-4244-4436-6/09/$25.00 ©2009 IEEE 978-1-4244-4436-6/09/$25.00 ©2009 IEEE

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Section: Giriúunclassified

Identification of nonlinear systems using Clonal Selection algorithm

Zorlu

Özer

2009

2009 IEEE 17th Signal Processing and Communications Applications Conference

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“…The satellite amplifier (usually driven to near the saturation point to get high gain) in digital satellite links is an example of communication systems with nonlinearity. Several researchers have studied nonlinear channel equalizers [49], nonlinear echo cancellation [1015], performance analysis of data transmission system [1620], and so on to improve the performance in communication systems with nonlinearity. Volterra filters have consequently achieved significant improvement with respect to linear digital filters.…”

Section: Introductionmentioning

confidence: 99%

The adaptive Volterra filter: its present and future

Kajikawa

2000

Electron. Comm. Jpn. Pt. III

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“…Doğrusal olmayan sistemlerin kimliklendirilmesinde, güç serisine dayalı kimliklendirme yaklaşımı, birçok sistemin davranışını modellemekte kullanılır. Volterra, Çiftdoğrusal (Bilinear) ve Çokterimli Özbağlanım (Polynomial Autoregressive, PAR) modelleri bunlardan sayılabilir [3][4][5].…”

Section: Introductionunclassified

A new multi volterra system model based on artificial neural network for nonlinear system identification

Özer¹,

Zorlu²

Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, 2005.

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ÖzetçeBu makalede doğrusal olmayan sistemlerin kimliklendirilmesi için yeni bir yapay sinir ağı tabanlı çoklu Volterra sistem modeli sunulmuştur. Yapılan bu çalışmada, sistem davranışının, girişine eklenen gürültüden etkilenmemesi ve sistemin arzulanan çıkışı üretmesi hedeflenmiştir. Sunulan modelin başarımı değişik Gauss dağılımlı gürültü seviyelerindeki test işaretleri ile denenmiş ve tekli Volterra sistem başarımları ile karşılaştırılmıştır. Benzetim sonuçları, önerilen modelin tekli Volterra sistemlere göre daha iyi performans verdiğini göstermektedir. AbstractIn this paper, a new multi Volterra system model based on artificial neural network for nonlinear system identification is proposed. In this work, our attention is especially focused on behaviours of system which aren't suffer from noise added to input and produce desired output. The proposed model were tested with Gaussian distribution noise signals at different levels and compared with single Volterra systems. The proposed method is illustrated by simulations. These simulations indicate that the performance of proposed model is better than single Volterra systems.

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Adaptive Volterra filtering with complete lattice orthogonalization

Cited by 12 publications

References 10 publications

Identification of nonlinear systems using Clonal Selection algorithm

Identification of nonlinear systems using Clonal Selection algorithm

The adaptive Volterra filter: its present and future

A new multi volterra system model based on artificial neural network for nonlinear system identification

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