Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design
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| Pubblicato in: | Electronics vol. 14, no. 4 (2025), p. 786 |
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| Autore principale: | |
| Altri autori: | |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14040786 |2 doi | |
| 035 | |a 3171004735 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Daylak, Funda |u Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul 34469, Turkey; Department of Electrical and Electronics Engineering, Altinbas University, Istanbul 34217, Turkey | |
| 245 | 1 | |a Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This 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. | |
| 653 | |a Data analysis | ||
| 653 | |a Design optimization | ||
| 653 | |a Simulation | ||
| 653 | |a Parameter identification | ||
| 653 | |a Datasets | ||
| 653 | |a Modelling | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Analog circuits | ||
| 653 | |a Signal processing | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Circuits | ||
| 653 | |a Chebyshev approximation | ||
| 653 | |a Filter design (mathematics) | ||
| 653 | |a Circuit design | ||
| 653 | |a Automation | ||
| 653 | |a Design standards | ||
| 653 | |a Transistors | ||
| 653 | |a Low pass filters | ||
| 653 | |a Dynamic range | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Ozoguz, Serdar |u Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul 34469, Turkey | |
| 773 | 0 | |t Electronics |g vol. 14, no. 4 (2025), p. 786 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171004735/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171004735/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171004735/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |