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    • Mathematics, Engineering and Computer Science Undergraduate Theses
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    Predicting Chaotic Behavior in Electrical Circuits

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    ScofieldT_Final_2019.pdf (8.008Mb)
    Author
    Scofield, Trey
    Advisor
    Kelly Cline; Eric Sullivan; Anthony Szpilka
    Date of Issue
    2019-04-01
    Subject Keywords
    forced RL-Diode, machine learning, neural network, chaos theory
    Metadata
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    URI
    https://scholars.carroll.edu/handle/20.500.12647/3516
    Title
    Predicting Chaotic Behavior in Electrical Circuits
    Type
    thesis
    Abstract
    Chaotic behavior is a natural phenomenon that can be found all around us in our daily lives. This project is focused on analyzing the behavior in forced RL-Diode (resistor, inductor, and diode) electrical circuits. We determined that when the sinusoidal input voltage of the circuit was increased, the voltage across the diode experienced period doubling, quadrupling, and then eventually chaos. Furthermore, this project is focused on predicting when and how these chaotic properties emerged from data that we collected. The primary machine learning technique that is used to predict chaos properties is a recurring neural network called an echo state network. Accurately predicting chaos in a smallscale electrical circuit like our RL-Diode circuit and further research on this topic could lead to a greater understanding of chaos theory and its applications in machine learning and electrical engineering.
    Degree Awarded
    Bachelor's
    Semester
    Spring
    Department
    Mathematics, Engineering & Computer Science
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