Determination of quantization intervals in rule based model for dynamic systems

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>>