System Identification Using Group Method of Data Handling (GMDH)

Abstract

Recently, many researchers have had much interest in various methods for system identifications. Such methods involve soft computing techniques such as neural networks and fuzzy logic. Neural networks and fuzzy logics are used to identify and predict nonlinear systems based on empirical data. However, using such methods, the nonlinear dynamics aren't explicitly expressed as a mathematical model. Hence, polynomial classifiers and networks were introduced to obtain a mathematical model for the nonlinear systems. However, polynomial classifiers require huge storage memory and can lead to instability when it uses higher order polynomials. Therefore, Group Method of Data Handling (GMDH) is introduced. GMDH is a multilayered network with a certain structure determined through training. It has the feature that the nonlinear dynamics are expressed as a mathematical model as well as the polynomial can have higher order terms without instability problems. In this thesis, the GMDH networks was implemented and then applied to the identification problem of 2000N MR damper. The GMDH network results were then compared with other nonlinear system identification method such as neural networks and polynomial classifiers. It was found that GMDH network can effectively emulate the behavior of a 2000N MR damper.