Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics

Abstract

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.

Keywords

• Chaos systems
• Explainable artificial
• Intelligence
• InterpretML
• Industrial robotics
• Machine learning

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