Determination of quantization intervals in rule based model for dynamic systems

Abstract

The authors introduce two adaptive procedures for quantizing continuous data used by symbolic empirical learning programs to generate rule-based models for dynamic systems. The basic idea is to use a top-down iterative procedure for refining quantization intervals selectively. In each iteration, the quantization interval having a maximum overall error rate is selected for refining. Each time a selected interval is divided into two new equal intervals. Based on the new quantization intervals, a new set of rules is generated and performance associated with each quantization interval is evaluated again. The refining procedure is applied repeatedly until a user-specified performance index is reached. The method was tested by two examples, one involving a simulated system, and the other a real life gas furnace.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>