A fuzzy inductive algorithm for Modeling dynamical systems in a comprehensible way
In this paper, we propose the use of temporal fuzzy chains for the modeling of dynamical systems in a way that is, comprehensible. We are interested in helping the overall understanding of the system execution, over and during a precise and finite time. To this end, we model its input/output behavior and how this has changed in the past. There is a double goal in mind: accuracy and interpretability. An inductive algorithm for analyzing finite continuous multivariate time series will be achieved, in which the use of fuzzy logic has been taken into account. The aim of the, algorithm is to help us to find changes in a system, as well as to identify the causes of these changes in a linguistic form. The causes will be specified by means of a set of fuzzy transitions between consecutive states, which consist of fuzzy rules that model the system. The method suggested has been applied on a real life case, human walk modeling.
induction algorithms fuzzy time-series mining machine learnin