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ID 116700
著者
Amoh, Seiya Tokushima University
Ogura, Miho Tokushima University
キーワード
chaos
bifurcation
symbolic differentiation
資料タイプ
学術雑誌論文
抄録
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.
掲載誌名
Nonlinear Theory and Its Applications, IEICE
ISSN
21854106
出版者
The Institute of Electronics, Information and Communication Engineers
13
2
開始ページ
440
終了ページ
445
発行日
2022-04-01
権利情報
©IEICE 2022
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言語
eng
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