Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots
Researchers have developed a new algorithm to identify explicit, piece-wise polynomial relationships within industrial time-series data. This method builds upon a prior algorithm that identified parsimonious implicit relationships, enabling anomaly detection. The new framework derives explicit representations using sets of polynomials from these implicit models, demonstrating its application in modeling the inverse dynamics of manipulator robots. Experiments on both 6-axis and 4-axis robots suggest these parsimonious models offer generalization capabilities compared to deep neural networks when encountering novel usage contexts. AI