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Bölüm: Işık University, School of Graduate Studies, Electric-Electronics Engineering M.S. Program × Konu: Artificial neural networks ×

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    Electrical circuit design based on neural networks
    (Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2026-01-23) Abou Allil, Feras; Köprü, Ramazan; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Elektrik-Elektronik Mühendisliği Yüksek Lisans Programı; Işık University, School of Graduate Studies, Electric-Electronics Engineering M.S. Program
    Artificial Neural Networks (ANNs) have gained significant attention due to their fast and accurate performance estimation capabilities, particularly in applications requiring strong learning and generalization. In this thesis, a comprehensive study is presented on the use of neural networks for the design and analysis of analog electronic circuits, focusing on both passive and active filter topologies. A feedforward neural network architecture is employed to reduce unwanted noise in measurement signals and to accurately infer component values from frequency response characteristics. For each circuit type, a dedicated neural network is trained to learn the relationship between circuit parameters and their corresponding magnitude responses. The study includes a variety of analog filters—such as low-pass and band-pass filters—implemented using passive elements as well as active devices including operational amplifiers and operational transconductance amplifiers (OTAs). Two training methodologies are introduced and evaluated: Element Spreading Training (EST) and Element Randomization Training (ERT). These approaches enhance dataset diversity and improve the neural network’s ability to generalize across a wider range of circuit behaviors, resulting in more reliable and robust predictions. The overall framework demonstrates the potential of integrating neural networks into classical analog circuit design, offering insights into performance, advantages, and limitations. All analyses and simulations are conducted and validated using MATLAB. The proposed methods have been tested under different frequency ranges and component tolerances.