Nazaré Thalita E, Nepomuceno Erivelton G, Martins Samir A M, Butusov Denis N
Control and Modelling Group (GCOM), Department of Electrical Engineering, Federal University of São João del-Rei, São João del-Rei, MG 36307-352, Brazil.
Youth Research Institute, Saint-Petersburg Electrotechnical University "LETI", 197376 Saint Petersburg, Russia.
Entropy (Basel). 2020 Aug 29;22(9):953. doi: 10.3390/e22090953.
An evergreen scientific feature is the ability for scientific works to be reproduced. Since chaotic systems are so hard to understand analytically, numerical simulations assume a key role in their investigation. Such simulations have been considered as reproducible in many works. However, few studies have focused on the effects of the finite precision of computers on the simulation reproducibility of chaotic systems; moreover, code sharing and details on how to reproduce simulation results are not present in many investigations. In this work, a case study of reproducibility is presented in the simulation of a chaotic jerk circuit, using the software LTspice. We also employ the OSF platform to share the project associated with this paper. Tests performed with LTspice XVII on four different computers show the difficulties of simulation reproducibility by this software. We compare these results with experimental data using a normalised root mean square error in order to identify the computer with the highest prediction horizon. We also calculate the entropy of the signals to check differences among computer simulations and the practical experiment. The methodology developed is efficient in identifying the computer with better performance, which allows applying it to other cases in the literature. This investigation is fully described and available on the OSF platform.
科学工作的可重复性是一个永恒的科学特征。由于混沌系统很难通过解析方法来理解，数值模拟在其研究中起着关键作用。在许多研究中，这种模拟被认为是可重复的。然而，很少有研究关注计算机有限精度对混沌系统模拟可重复性的影响；此外，许多研究中没有代码共享以及关于如何重现模拟结果的细节。在这项工作中，使用LTspice软件对一个混沌 jerk 电路进行模拟，给出了一个可重复性的案例研究。我们还利用OSF平台来分享与本文相关的项目。在四台不同的计算机上使用LTspice XVII进行的测试表明了该软件在模拟可重复性方面存在的困难。我们使用归一化均方根误差将这些结果与实验数据进行比较，以确定具有最长预测时间范围的计算机。我们还计算信号的熵，以检查计算机模拟与实际实验之间的差异。所开发的方法在识别性能更好的计算机方面是有效的，这使得它能够应用于文献中的其他案例。这项研究在OSF平台上有完整的描述且可供使用。
