Article Fingerprint
ReserarchID
473R1
This paper is an extension of previous work that addressed the application of bipolar transistor amplifier design using neural networks. That work addressed the design of common emitter amplifiers by first mathematically determining specific output parameters from a large selection of biasing resistors. Once the outputs had been determined, a neural network was trained, using the aforementioned results as inputs and the biasing resistors as outputs. This was initially performed with ideal emitter bypass capacitors, but was then followed-up by employing several non-ideal capacitors, making it much more interesting and useful. This paper focuses on the common collector and the common base configurations. Bipolar junction transistor amplifier parameters often include voltage gain, input impedance, output impedance, and the voltage difference between the collector and emitter. These will be addressed in this paper as before.
Thomas L. Hemminger. 2018. \u201cUsing Neural Networks to Design Transistor Amplifier Circuits\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 18 (GJCST Volume 18 Issue D1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.
Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.
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): 258
Total Views (Real + Logic): 5914
Total Downloads (simulated): 1557
Publish Date: 2018 04, Fri
Monthly Totals (Real + Logic):
This paper attempted to assess the attitudes of students in
Advances in technology have created the potential for a new
Inclusion has become a priority on the global educational agenda,
This paper is an extension of previous work that addressed the application of bipolar transistor amplifier design using neural networks. That work addressed the design of common emitter amplifiers by first mathematically determining specific output parameters from a large selection of biasing resistors. Once the outputs had been determined, a neural network was trained, using the aforementioned results as inputs and the biasing resistors as outputs. This was initially performed with ideal emitter bypass capacitors, but was then followed-up by employing several non-ideal capacitors, making it much more interesting and useful. This paper focuses on the common collector and the common base configurations. Bipolar junction transistor amplifier parameters often include voltage gain, input impedance, output impedance, and the voltage difference between the collector and emitter. These will be addressed in this paper as before.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.