Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Wafaa Al Nassan; Talal Bonny; Mohammad Al-Shabi

doi:10.1117/12.3013883

7 June 2024 Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks

Wafaa Al Nassan, Talal Bonny, Mohammad Al-Shabi

Author Affiliations +

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130511R (2024) https://doi.org/10.1117/12.3013883
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

This Chaos theory has long been a fascinating realm of study, offering insights into systems characterized by sensitivity to initial conditions and complex, unpredictable behaviour. Among these intriguing systems, the "Capsule-Shaped Equilibrium Curve Chaotic System" stands out due to its distinctive and intricate dynamics. In this paper, we present a novel approach to understanding and predicting the behaviour of this complex chaotic system through the application of Recurrent Neural Networks (RNNs). Our investigation begins with a thorough examination of the capsule-shaped equilibrium curve chaotic system, revealing its underlying principles and revealing its chaotic nature. We employ the scalability of neural networks to propose an innovative approach for predicting the temporal progression of this system. A promising avenue for modeling the dynamic behavior of chaotic systems with a high degree of precision is offered by the RNN framework, which is capable of capturing temporal dependencies. We delve into the details of our prediction methodology, including data preprocessing, network architecture, and training strategies tailored to the unique characteristics of the capsule-shaped equilibrium curve chaotic system. We conduct extensive experiments and provide quantitative evaluations of prediction precision and dependability to evaluate the predictive capabilities of our neural network-based strategy. Furthermore, this investigation contributes to the comprehension of chaotic systems and opens the door to practical applications in diverse fields, such as physics, engineering, and finance, where precise predictions of chaotic dynamics are essential for making decisions and controlling systems

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Wafaa Al Nassan, Talal Bonny, and Mohammad Al-Shabi "Predicting the dynamics of a capsule-shaped equilibrium curve chaotic system using recurrent neural networks", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130511R (7 June 2024); https://doi.org/10.1117/12.3013883

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