Fuzzy Association Rule Mining for Data Driven Analysis of Dynamical Systems

Abstract

In system identification a key step is to find a suitable model structure. The utilizations of prior knowledge and physical insight about the system are very important when selecting the model structure. In nonlinear black-box modeling no physical insight is available we have “only” observed inputs and outputs from the dynamical system. Association rule mining is one of the widely used data mining tools. It finds interesting association or correlation relationships among a large data set. The aim of this paper is to demonstrate that this data mining tool can be effectively applied for the datadriven modeling and analysis of dynamical systems. The detected association rules can be interpreted as simple local input-output models of the modeled process. Hence, the analysis of the mined association rules (models) can provide useful information about the structure and the order of the model that can adequately describe the dynamical behavior of the process. In this paper a fuzzy association rule mining algorithm is introduced and a rule-base simplification algorithm is presented for the generation of a set of “rule-based models” that can be directly used as a qualitative model of the system. The general applicability of the developed tool is illustrated by the analysis of the input-output data of a continuously stirred styrene polymerization reactor. The detected association rules is used for the selection of the structure of a linear and nonlinear (neural network) models for this process and determine the most relevant process variables.