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    An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications
    (Mdpi, 2022) Alagoz, Baris Baykant; Simsek, Ozlem Imik; Ari, Davut; Tepljakov, Aleksei; Petlenkov, Eduard; Alimohammadi, Hossein
    Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality. Secondly, the multiplicative neuron model is modified to develop Power-Weighted Multiplicative (PWM) neural models. The modified PWM neuron model involves the power-weighted multiplicative units similar to dendritic branches of biological neurons, and this neuron model can better represent polynomial nonlinearity and they can operate in the real-valued neuron mode, complex-valued neuron mode, and the mixed-mode. In this study, the EFO-GS algorithm is used for the training of the PWM neuron models to perform an efficient neuroevolutionary computation. Authors implement the proposed PWM neural processing with the EFO-GS in an electronic nose application to accurately estimate Nitrogen Oxides (NOx) pollutant concentrations from low-cost multi-sensor array measurements and demonstrate improvements in estimation performance.
  • Küçük Resim Yok
    Öğe
    Optimal architecture artificial neural network model design with exploitative alpha gray wolf optimization for soft calibration of CO concentration measurements in electronic nose applications
    (Sage Publications Ltd, 2023) Simsek, Ozlem Imik; Alagoz, Baris Baykant
    The low-cost and small size solid-state sensor arrays are suitable to implement a wide-area electronic nose (e-nose) for real-time air quality monitoring. However, accuracy of these low-cost sensors is not adequate for precise measurements of pollutant concentrations. Artificial neural network (ANN) estimation models are used for the soft calibration of low-cost sensor array measurements and significantly improve the accuracy of low-cost multi-sensor measurements. However, optimality of neural architecture affects the performance of ANN estimation models, and optimization of the ANN architecture for a training data set is essential to improve data-driven modeling performance of ANNs to reach optimal neural complexity and improved generalization. In this study, an optimal architecture ANN estimator design scheme is suggested to improve the estimation performance of ANN models for e-nose applications. To this end, a gray wolf optimization (GWO) algorithm is modified, and an exploitative alpha gray wolf optimization (EA-GWO) algorithm is suggested. This modification enhances local exploitation skill of the best alpha gray wolf search agent, and thus allows the fine-tuning of ANN architectures by minimizing a multi-objective cost function that implements mean error search policy. Experimental study demonstrates the effectiveness of optimal architecture ANN models to estimate CO concentration from the low-cost multi-sensor data.
  • Küçük Resim Yok
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    Transfer Fonksiyonlarının Gerçeklenmesi için İki Analitik Ayrıklaştırma Yönteminin Performans Değerlendirmesi
    (2019) Simsek, Ozlem Imik; Alagöz, Barış Baykant
    Yakın tarihli araştırmalar kesirli aritmetiğin gerçek sistemlerin daha doğru modellemesini sağladığı rapor etmiştir. Bu nedenle, kesir dereceli sistem modelleri simülasyon ve nümerik analizlerde yaygın olarak faydalanılmaya başlandı. Ancak, ayrık zaman gerçeklemelerinin yüksek işlem karmaşıklığından dolayı mühendislik uygulamalarının çalışma aralıkları içinde kesir dereceli eleman ve transfer fonksiyonlarının yeterli doğrulukta sayısal olarak gerçeklenmesine ihtiyaç duyulmaktadır. Bu çalışma uygulama bakış açısı ile iki analitik ayrık yakınsama yaklaşımının frekans cevabı eşleşme özelliklerini incelemektedir: Bunlardan biri Tustin özyinelemeli yakınsaması yöntemi olarak bilinen doğrudan ayrıklaştırma yöntemidir ve diğeri kesir dereceli türev operatörünün sürekli kesir açılımından (CFE) faydalanan dolaylı bir ayrıklaştırma yaklaşımıdır. Bu iki yöntemin sonuçları karşılaştırılmakta ve yöntemlerin uygulanabilirlikleri, kontrol sistemleri ve filtre gerçekleme uygulamalarının çalışma frekans aralıkları gereksinimleri temelinde tartışılmaktadır.