Determination of quantization intervals in rule based model for dynamic systems

Author

Chan, Chien-Chung ; Batur, Celal ; Srinivasan, Arvind

Author_Institution

Akron Univ., OH, USA

fYear

1991

fDate

13-16 Oct 1991

Firstpage

1719

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

Keywords

adaptive systems; decision theory; discrete time systems; iterative methods; learning systems; performance index; adaptive systems; decision tree; dynamic systems; error rate; gas furnace; learning systems; quantization intervals; rule based model; symbolic empirical learning programs; top-down iterative procedure; user-specified performance index; Buildings; Decision trees; Furnaces; Life testing; Mathematical model; Mechanical engineering; Production; Quantization; Refining; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on

Conference_Location

Charlottesville, VA

Print_ISBN

0-7803-0233-8

Type

conf

DOI

10.1109/ICSMC.1991.169942

Filename

169942