Amoh, Seiya Tokushima University
Ogura, Miho Tokushima University
上田, 哲史 Tokushima University 徳島大学 教育研究者総覧 KAKEN研究者をさがす
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.
Nonlinear Theory and Its Applications, IEICE
The Institute of Electronics, Information and Communication Engineers
nolta_13_2_440.pdf 1.76 MB