Authors:
Ewa Dudek
1
and
Tadeusz Dyduch
2
Affiliations:
1
Institute of Automatics, AGH University of Science and Technology, Poland
;
2
Institute of Computer Science, AGH University of Science and Technology, Poland
Keyword(s):
Control, knowledge acquisition, knowledge representation, learning system, discrete manufacturing process.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
Abstract:
The aim of the paper is to present a conception of a hybrid learning method for discrete manufacturing processes control.The method is based on a special form of a knowledge based model of discrete manufacturing process, named here hybrid knowledge based model (HKBM). The model consists of two parts, each of a different type of model: algebraic-logical model in a state spacethat is created on a basis of process technology description and set of expert rules referring to control. A general scheme of HKBM of a vast class of discrete manufacturing processes (DMP) is given in the paper. Then the method of synthesis of intelligent, learning algorithms that use information on the process gained in previous iterations as well as an expert knowledge is described. To illustrate the presented ideas, the scheduling algorithm for a special NP-hard problem is given.