Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design

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dc.contributor.authorDaylak, Funda
dc.contributor.authorÖzoğuz, Serdar
dc.date.accessioned2025-05-13T05:48:56Z
dc.date.available2025-05-13T05:48:56Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
dc.description.abstractThis study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit configurations, was used to train and validate the model, providing an accurate representation of the entire dataset without requiring full-scale data analysis. In forward modeling, the same subset was used to train the network, which was then used to predict DR values for the remaining dataset. This approach enabled the identification of circuit parameters that resulted in optimal DR values. The results confirm the effectiveness of these techniques, with both inverse modeling and forward modeling outperforming the standard circuit design. At 160 kHz, a critical frequency for the operation of the designed filter, inverse modeling achieved a DR of 140.267 dB and forward modeling reached 136.965 dB, compared to 132.748 dB for the standard circuit designed using the traditional approach. These findings demonstrate that ANN-based methods can significantly enhance design accuracy, reduce time requirements, and improve energy efficiency in analog circuit optimization.
dc.identifier.citationDaylak, F., & Ozoguz, S. (2025). Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design. Electronics, 14(4), 786.
dc.identifier.doi10.3390/electronics14040786
dc.identifier.issn2079-9292
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85218862597
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5745
dc.identifier.volume14
dc.identifier.wosWOS:001431728300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorDaylak, Funda
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofElectronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectautomated circuit design
dc.subjectcircuit parameter prediction
dc.subjectanalog circuit design
dc.subjectartificial neural networks (ANN)
dc.subjectdynamic range optimization
dc.subjectChebyshev low-pass filter design
dc.subjectneural network-based optimization
dc.titleAutomated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design
dc.typeArticle

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