Parçacık Sürü Optimizasyonuna Dayalı Sinir Ağları Kullanılarak Ultra Geniş Bant Kablosuz Kapsül Endoskopide Vücut İçi Mesafe Ölçümü

Öz

Bu makalede, ultra geniş bant (UWB) sinyalleri kullanan bir kablosuz kapsül endoskopunun lokalizasyonu için hassas vücut içi mesafe ölçümü problemi ele alınmaktadır. Bu bağlamda, yapay sinir ağları ve metaheuristik temelli öğrenme algoritmalarının (örnek olarak parçacık sürüsü optimizasyonu (PSO) ortak kullanımı irdelenmektedir. Makalenin literatüre katkıları şu şekilde özetlenebilir. İlk olarak, UWB tabanlı vücut içi mesafe ölçümü için PSO algoritmasının sistematik bir performans analizi yapılmış ve söz konusu problemin çözümü için PSO algoritmasının geliştirilmiş bir versiyonu önerilmiştir. İkinci olarak, önerilen PSO algoritmasının performansı Bayesian Regularization, Levenberg-Marquardt ve Single Conjugate Gradient gibi geleneksel öğrenme algoritmaları ile karşılaştırılmıştır. Son olarak, yapay sinir ağlarında kullanılan aktivasyon fonksiyonlarının performans üzerindeki etkileri incelenmiştir. Elde edilen sonuçlar, önerilen PSO algoritması vasıtası ile geleneksel tekniklere nazaran % 44’e varan performans artışları elde edilebileceğini göstermektedir.

Anahtar Kelimeler

• Vücut içi mesafe ölçümü,Metasezgisel algoritmalar,Sinir ağları,Parçacık sürü optimizasyonu,Ultra geniş bant,Kablosuz kapsül endoskopi

IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

Abstract

We consider the problem of accurate in-body ranging for localization of a wireless capsule endoscope utilizing ultra-wide band (UWB) signaling. In this context, we explore the joint use of neural network structures and learning algorithms based on metaheuristics, an example of which is particle swarm optimization (PSO). The contributions of this paper are three-fold. First, we undertake a systematic performance analysis of the PSO technique for UWB-based in-body ranging and propose an improved version of the PSO algorithm. Second, we quantitatively compare the performance of PSO techniques against more traditional learning algorithms, such as Bayesian Regularization, Levenberg-Marquardt and Single Conjugate Gradient. Third, we quantify the impact of activation functions used to define the neural network structure on performance. Our results indicate that PSO-based techniques can outperform traditional techniques by as much as 44%, depending on the activation functions used in the neural network.

Keywords

• In-body ranging,Metaheuristics algorithms,Neural network,Particle swarm optimization,Ultra-Wide band,Wireless capsule endoscopy

Kaynakça

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