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Keywords = subtract-and-sum circuit

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16 pages, 4091 KB  
Article
M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers
by Malinka Ivanova and Mariana Durcheva
Mathematics 2023, 11(4), 1001; https://doi.org/10.3390/math11041001 - 15 Feb 2023
Viewed by 2199
Abstract
The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper [...] Read more.
The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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17 pages, 4099 KB  
Article
A Temperature-Compensation Technique for Improving Resolver Accuracy
by Wandee Petchmaneelumka, Vanchai Riewruja, Kanoknuch Songsuwankit and Apinai Rerkratn
Sensors 2021, 21(18), 6069; https://doi.org/10.3390/s21186069 - 10 Sep 2021
Cited by 3 | Viewed by 3348
Abstract
Variation in the ambient temperature deteriorates the accuracy of a resolver. In this paper, a temperature-compensation technique is introduced to improve resolver accuracy. The ambient temperature causes deviations in the resolver signal; therefore, the disturbed signal is investigated through the change in current [...] Read more.
Variation in the ambient temperature deteriorates the accuracy of a resolver. In this paper, a temperature-compensation technique is introduced to improve resolver accuracy. The ambient temperature causes deviations in the resolver signal; therefore, the disturbed signal is investigated through the change in current in the primary winding of the resolver. For the proposed technique, the primary winding of the resolver is driven by a class-AB output stage of an operational amplifier (opamp), where the primary winding current forms part of the supply current of the opamp. The opamp supply-current sensing technique is used to extract the primary winding current. The error of the resolver signal due to temperature variations is directly evaluated from the supply current of the opamp. Therefore, the proposed technique does not require a temperature-sensitive device. Using the proposed technique, the error of the resolver signal when the ambient temperature increases to 70 °C can be minimized from 1.463% without temperature compensation to 0.017% with temperature compensation. The performance of the proposed technique is discussed in detail and is confirmed by experimental implementation using commercial devices. The results show that the proposed circuit can compensate for wide variations in ambient temperature. Full article
(This article belongs to the Collection Instrument and Measurement)
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