In this study, the generalizability and distributivity of three different chaotic systems within an industrial robotics time series dataset are explored using an annotated artificial intelligence algorithm. A time series dataset derived from industrial robotics processes was constructed and transformed into the Runge-Kutta system, comprising fourth-order differential equations for normalization. Among the processed data, variables related to x-y-z positions underwent chaotic transformations through Lorenz, Chen, and Rossler chaos systems. The x variable and angle variables from the transformed x-y-z data were inputted into the InterpretML model, an annotated artificial intelligence model, to elucidate the effects of angle variables on the x position variable. As a result of this analysis, InterpretML Local analysis revealed a sensitivity of 0.05 for the Rossler chaos system, 0.15 for Chen, and 0.25 for Lorenz. Furthermore, global analysis indicated precision rates of 0.17 for Rossler, 0.255 for Chen, and 0.35 for Lorenz chaos systems. These sensitivity results suggest that the Rossler chaos system consistently provides more accurate results in both InterpretML local and global analyses compared to other chaotic systems. This study contributes significantly to the literature by analyzing the distributive and generalization properties of chaos systems and enhancing understanding of these systems.
Chaos systems Explainable artificial Intelligence InterpretML Industrial robotics Machine learning
The author would like to thank all the data sets, materials, information sharing and support used in the assembly of this article.
Primary Language | English |
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Subjects | Reinforcement Learning |
Journal Section | Articles |
Authors | |
Early Pub Date | October 28, 2024 |
Publication Date | October 31, 2024 |
Submission Date | April 20, 2024 |
Acceptance Date | June 27, 2024 |
Published in Issue | Year 2024 Volume: 8 Issue: 4 |