COMPARISON OF PERFORMANCE OF DIFFERENT SAMPLING METHODS IN ARTIFICIAL NEURAL NETWORKS

Year 2022, , 412 - 428, 30.06.2022

Ahmet Uluslu

https://doi.org/10.21923/jesd.1012106

Abstract

In optimization modeling problems, the selection of the data to be used is of great importance. In this study, the effect of different sampling methods on the performance of Artificial Neural Networks is compared on the microstrip patch antenna by using the Multilayer Perceptron, which is the model that is frequently used in antenna designs. The selected neural network black box model consists of 5 input and 1 output parameters. The application of Latin Hypercube and Monte Carlo sampling methods was examined in the selection of data to be used in microstrip patch antenna modeling. First of all, Latin Hypercube and Monte Carlo samples were obtained according to their unique creation method in the specified value ranges of the sample number (training and validation data sets) input parameters. Afterwards, the problems with 2 different sample numbers were randomly separated at a rate of 50% and 33%, and training and validation data were created. In the performance comparison, a total of 12 different networks with 3 algorithms and 4 different architectural structures, as well as 4 different training and validation data sets are used. When the results are compared with each other, it is seen that Monte Carlo sampling method gives more successful results in terms of performance in modeling with low or high educational sample number. On the other hand, it is seen that the Latin Hypercube sampling method, on the other hand, increased the number of training samples and caused a partial improvement. However, it still lags behind the other Monte Carlo sampling method, which has less sample size, in terms of performance. Therefore, it was concluded that the Monte Carlo sampling method is more applicable for this and similar problems.

Keywords

ANN, MLP, Latin Hypercube, Monte Carlo, Modelling.

FARKLI ÖRNEKLEME YÖNTEMLERİNİN YAPAY SİNİR AĞLARINDA PERFORMANSININ KARŞILAŞTIRILMASI

Abstract

Optimizasyon modelleme problemlerinde, kullanılacak verinin seçimi büyük önem arz etmektedir. Bu çalışmada farklı örnekleme yöntemlerinin Yapay Sinir Ağlarının başarısındaki etkisini, anten tasarımlarında sıkça kullanılan model olan Çok Katmanlı Algılayıcı kullanılarak mikroşerit yama anteni üzerinde performans karşılaştırılması yapılmaktadır. Seçilen Yapay Sinir Ağı kara kutu modeli 5 giriş ve 1 çıkış parametrelerinden oluşmaktadır. Mikroşerit yama anten modellenmesinde kullanılacak veri seçiminde Latin Hiperküp ve Monte Carlo örnekleme yönteminin uygulanması incelenmiştir. Öncelikle örnek sayısı (eğitim ve doğrulama veri setleri) giriş parametrelerinin belirlenen değer aralıklarında Latin Hiperküp ve Monte Carlo örnekleri kendilerine özgün oluşturulma yöntemine göre temin edilmiştir. Akabinde oluşturulan 2 farklı örnek sayısına sahip problemler kendi içlerinde %50 ve %33 oranında rastgele ayrılarak eğitim ve doğrulama verileri oluşturulmuştur. Performans karşılaştırmasında, 3 algoritma ile 4 farklı mimari yapıya sahip toplamda 12 farklı ağ ile birlikte 4 farklı sayıda eğitim ve doğrulama veri setleri kullanılmaktadır. Çıkan sonuçlar birbirleri ile mukayese edildiğinde, düşük veya yüksek eğitim örnek sayısına sahip modellemede de Monte Carlo örnekleme yönteminin performans olarak daha başarılı sonuçlar verdiği görülmektedir. Buna karşın kendi içinde Latin Hiperküp örnekleme yönteminin ise eğitim örnek sayısında artışa gidilmesi kısmi iyileşmeye neden olduğu görülmektedir. Fakat yine de daha az örnek sayısına sahip olan diğer Monte Carlo örnekleme yönteminin performans olarak gerisinde kalmıştır. Dolayısı ile Monte Carlo örnekleme yönteminin bu ve benzer problemler için daha uygulanabilir olduğuna kanaat getirilmiştir.

Keywords

YSA, ÇKA, Latin Hiperküp, Monte Carlo, Modelleme.

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