Multistrategy learning approaches to generate and tune fuzzy control structures and their application in manufacturing

摘要

Intelligence is strongly connected with learning adapting abilities, therefore such capabilities areconsidered as indispensable features of intelligent manufacturing systems (IMSs). A number ofapproaches have been described to apply different machine learning (ML) techniques formanufacturing problems, starting with rule induction in symbolic domains and pattern recognitiontechniques in numerical, subsymbolic domains. In recent years, artificial neural network (ANN)based learning is the dominant ML technique in manufacturing. However, mainly because of the'black box' nature of ANNs, these solutions have limited industrial acceptance. In the paper, theintegration of neural and fuzzy techniques is treated and former solutions are analysed. A geneticalgorithm (GA) based approach is introduced to overcome problems that are experienced duringmanufacturing applications with other algorithms.