Using Neural Networks to Design Transistor Amplifier Circuits

Using Neural Networks to Design Transistor Amplifier Circuits

Thomas L. Hemminger

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Pennsylvania State University

Using Neural Networks to Design Transistor Amplifier Circuits

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Abstract

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.

References

8 Cites in Article

Reference Format

T Hemminger (2016). A Neural Network Approach to Transistor Circuit Design.
A Sedra,K Smith (2015). Microelectronic Circuits.
R Jaeger (1997). Microelectronic Circuit Design.
D Neamen (2010). Microelectronics Circuit Analysis and Design.
T Hemminger (2005). Understanding Transmission Line Impedance Matching Using Neural Networks and PowerPoint.
Y Pao,H (1989). Adaptive Pattern Recognition and Neural Networks.
Daniel Graupe (2013). Principles of Artificial Neural Networks.
M Hagan,H Demuth,B Demuth,H Beale,M Neural Network Design.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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).

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GJCST Volume 18 Issue D1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-D Classification F.1.1

I.5.1

Submission ReceivedDecember 11, 2017
Peer Review Double Blind
Handling Editor
Accepted January 2, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

April 13, 2018

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Article Matrices

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): 280

Total Views (Real + Logic): 5959

Total Downloads (simulated): 1588

Publish Date: 2018 04, Fri

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Total Views: 5959

Total Downloads: 1588

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