Investigation of the Influence of Computation Accuracy in the Implementation of Chaotic Systems in Python for Secure Telecommunication Systems
DOI:
https://doi.org/10.31861/sisiot2024.1.01003Keywords:
visualization of chaotic models, Python, dynamic systems, chaotic systems, Chua, Lorenz and Rössler modelsAbstract
This article focuses on the use of the Python programming language to visualize chaotic models and for the investigation of the influence of initial conditions in physical systems, in particular, the Chua, Lorenz, and Rössler models. Chaotic systems are dynamic and sensitive to initial conditions, making them unpredictable as to how they will behave and react. This means that in the long run, very different outcomes can result from even small changes in initial conditions. Chaotic systems are studied in a variety of scientific fields, including physics, mathematics, biology, engineering and economics. Python, the world's most popular scientific programming language, transforms complex models into intuitive visualizations. The paper reveals the capabilities of various Python algorithms and libraries used to visualize these models, taking into account their specifics. The article focuses on three chaotic models: the Chua system, which is a universal example of a chaotic system; the Lorenz attractor, which is famous for its chaotic properties; and the Rössler rotational oscillator, which is widely used in such fields as biology, chemistry, physics, and engineering. Each model is studied in detail, its key characteristics and parameters are presented, and graphs of these models are displayed by means of Python simulation. Python, due to its ease of use and high performance, makes it possible to solve such tasks quickly and efficiently. Finally, the authors share their conclusions on the importance of initial conditions for Lorenz, Rössler and Chua systems, as well as their impact on telecommunication systems. This study provides insight into how Python, a programming language with a high level of abstraction, allows for the rapid and efficient development of complex algorithms and models needed to deal with chaotic systems. It also allows researchers and engineers to develop efficient algorithms for signal processing and control of telecommunication systems.
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