Machine Learning and Rules Induction in Support of Analog Amplifier Design
The aim of the paper is to present a two-step method for facilitating the design of analog amplifiers taking into account the bottom–top approach and utilizing machine learning techniques. The X-chart and a framework describing the specificity of analog circuit design using machine learning are intr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/10/9/145 |
| _version_ | 1797489811128320000 |
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| author | Malinka Ivanova Miona Andrejević Stošović |
| author_facet | Malinka Ivanova Miona Andrejević Stošović |
| author_sort | Malinka Ivanova |
| collection | DOAJ |
| description | The aim of the paper is to present a two-step method for facilitating the design of analog amplifiers taking into account the bottom–top approach and utilizing machine learning techniques. The X-chart and a framework describing the specificity of analog circuit design using machine learning are introduced. The possibility of libraries with open machine learning models to support the designer is also discussed. The proposed method is verified for a three-stage amplifier design. In the first step, the stage type is predicted with 89.74% accuracy as the applied learner is a Decision Tree machine learning algorithm. Moreover, two induction rule algorithms are used for predictive logic generation. In the second step, some typical parameters for a given stage are predicted considering four learners: Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine. The most suitable is found to be Support Vector Machine, which is characterized with the smallest obtained errors. |
| first_indexed | 2024-03-10T00:21:59Z |
| format | Article |
| id | doaj.art-9e9c70e0726e4f96a07f5a802bfc9d60 |
| institution | Directory Open Access Journal |
| issn | 2079-3197 |
| language | English |
| last_indexed | 2024-03-10T00:21:59Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj.art-9e9c70e0726e4f96a07f5a802bfc9d602023-11-23T15:41:24ZengMDPI AGComputation2079-31972022-08-0110914510.3390/computation10090145Machine Learning and Rules Induction in Support of Analog Amplifier DesignMalinka Ivanova0Miona Andrejević Stošović1Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, Sofia 1797, BulgariaDepartment of Electronics, Faculty of Electronic Engineering, University of Niš, Niš 18000, SerbiaThe aim of the paper is to present a two-step method for facilitating the design of analog amplifiers taking into account the bottom–top approach and utilizing machine learning techniques. The X-chart and a framework describing the specificity of analog circuit design using machine learning are introduced. The possibility of libraries with open machine learning models to support the designer is also discussed. The proposed method is verified for a three-stage amplifier design. In the first step, the stage type is predicted with 89.74% accuracy as the applied learner is a Decision Tree machine learning algorithm. Moreover, two induction rule algorithms are used for predictive logic generation. In the second step, some typical parameters for a given stage are predicted considering four learners: Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine. The most suitable is found to be Support Vector Machine, which is characterized with the smallest obtained errors.https://www.mdpi.com/2079-3197/10/9/145analog designmachine learningamplifier circuitsX-chartframework for analog circuits design |
| spellingShingle | Malinka Ivanova Miona Andrejević Stošović Machine Learning and Rules Induction in Support of Analog Amplifier Design Computation analog design machine learning amplifier circuits X-chart framework for analog circuits design |
| title | Machine Learning and Rules Induction in Support of Analog Amplifier Design |
| title_full | Machine Learning and Rules Induction in Support of Analog Amplifier Design |
| title_fullStr | Machine Learning and Rules Induction in Support of Analog Amplifier Design |
| title_full_unstemmed | Machine Learning and Rules Induction in Support of Analog Amplifier Design |
| title_short | Machine Learning and Rules Induction in Support of Analog Amplifier Design |
| title_sort | machine learning and rules induction in support of analog amplifier design |
| topic | analog design machine learning amplifier circuits X-chart framework for analog circuits design |
| url | https://www.mdpi.com/2079-3197/10/9/145 |
| work_keys_str_mv | AT malinkaivanova machinelearningandrulesinductioninsupportofanalogamplifierdesign AT mionaandrejevicstosovic machinelearningandrulesinductioninsupportofanalogamplifierdesign |