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Transistor amplifier design is an important and fundamental concept in electronics, typically encountered by students at the junior level in electrical engineering. This paper focuses on two configurations that employ neural networks to design bipolar junction transistor circuits. The purpose of this work is to determine which design best fits the required parameters. Engineers often need to develop transistor circuits using a particular topology, e.g., common emitter, common collector, or common base. These also include a set of parameters including voltage gain, input impedance, and output impedance. For the most part, there are several methodologies that can provide a suitable solution, however the objective of this work is to indicate which external resistors are necessary to yield useful designs by employing neural networks.
Thomas L. Hemminger. 2016. \u201cA Neural Network Approach to Transistor Circuit Design\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 16 (GJCST Volume 16 Issue D1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 131
Country: United States
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Thomas L. Hemminger (PhD/Dr. count: 0)
View Count (all-time): 252
Total Views (Real + Logic): 7289
Total Downloads (simulated): 1945
Publish Date: 2016 12, Fri
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Inclusion has become a priority on the global educational agenda,
Transistor amplifier design is an important and fundamental concept in electronics, typically encountered by students at the junior level in electrical engineering. This paper focuses on two configurations that employ neural networks to design bipolar junction transistor circuits. The purpose of this work is to determine which design best fits the required parameters. Engineers often need to develop transistor circuits using a particular topology, e.g., common emitter, common collector, or common base. These also include a set of parameters including voltage gain, input impedance, and output impedance. For the most part, there are several methodologies that can provide a suitable solution, however the objective of this work is to indicate which external resistors are necessary to yield useful designs by employing neural networks.
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