Title :
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
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;
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
DOI :
10.1109/ICSMC.1991.169942
