Measuring Dependencies between Variables of a Dynamical System Using Fuzzy Affiliations

Abstract

A statistical, data-driven method is presented that quantifies influences between variables of a dynamical system. The method is based on finding a suitable representation of points by fuzzy affiliations with respect to landmark points using the Scalable Probabilistic Approximation algorithm. This is followed by the construction of a linear mapping between these affiliations for different variables and forward in time. This linear mapping, or matrix, can be directly interpreted in light of unidirectional dependencies, and relevant properties of it are quantified. These quantifications, given by the sum of singular values and the average row variance of the matrix, then serve as measures for the influences between variables of the dynamics. The validity of the method is demonstrated with theoretical results and on several numerical examples, covering deterministic, stochastic, and delayed types of dynamics. Moreover, the method is applied to a non-classical example given by real-world basketball player movement, which exhibits highly random movement and comes without a physical intuition, contrary to many examples from, e.g., life sciences.

Equivalent-Input-Disturbance Based Robust Control Design for Fuzzy Semi-Markovian Jump Systems via the Proportional-Integral Observer Approach

“…The study based on the Takagi-Sugeno (T-S) fuzzy model technique has attracted a lot of attention due to its remarkable ability to approximate complex nonlinear systems as the weighted sum of linearized subsystems at operating points using fuzzy membership functions and if-then rules. In recent decades, remarkable research attention has been given to the stability and stabilization for T-S fuzzy systems [1][2][3][4][5][6]. In [7,8], the authors looked into the H ∞ filtering problem for nonlinear switched systems with time-varying delay by incorporating T-S fuzzy model characteristics.…”