ID | 116700 |
Author |
Amoh, Seiya
Tokushima University
Ogura, Miho
Tokushima University
Ueta, Tetsushi
Tokushima University
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Keywords | chaos
bifurcation
symbolic differentiation
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Content Type |
Journal Article
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Description | In the conventional implementations for solving bifurcation problems, Jacobian matrix and its partial derivatives regarding the given problem should be provided manually. This process is not so easy, thus it often induces human errors like computation failures, typing error, especially if the system is higher order. In this paper, we develop a preprocessor that gives Jacobian matrix and partial derivatives symbolically by using SymPy packages on the Python platform. Possibilities about the inclusion of errors are minimized by symbolic derivations and reducing loop structures. It imposes a user only on putting an expression of the equation into a JSON format file. We demonstrate bifurcation calculations for discrete neuron dynamical systems. The system includes an exponential function, which makes the calculation of derivatives complicated, but we show that it can be implemented simply by using symbolic differentiation.
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Journal Title |
Nonlinear Theory and Its Applications, IEICE
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ISSN | 21854106
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Publisher | The Institute of Electronics, Information and Communication Engineers
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Volume | 13
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Issue | 2
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Start Page | 440
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End Page | 445
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Published Date | 2022-04-01
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Rights | ©IEICE 2022
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
Publisher
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departments |
Center for Administration of Information Technology
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