| Autors: Ivanova, M. S., Stošović M. A. Title: Machine Learning and Rules Induction in Support of Analog Amplifier Design Keywords: analog design; machine learning; amplifier circuits; X-chart Abstract: 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 error References Issue
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Цитирания (Citation/s):
1. Lagos-Eulogio P, Miranda-Romagnoli P, Seck-Tuoh-Mora JC, Hernández-Romero N. Improvement in Sizing Constrained Analog IC via Ts-CPD Algorithm. Computation. 2023; 11(11):230. https://doi.org/10.3390/computation11110230 - 2023 - в издания, индексирани в Scopus или Web of Science
2. Litovski V., Lecture Notes in Analogue Electronics: Electronic Signal Amplification and Linear Oscillators, Lecture Notes in Electrical EngineeringVolume 1074, Springer Science and Business Media Deutschland GmbH. 2023; 1 - 674. ISSN: 18761100, DOI: 10.1007/978-981-99-5095-9 - 2023 - в издания, индексирани в Scopus или Web of Science
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus
