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

Intelligence is strongly connected with learning adapting abilities, therefore such capabilities are considered as indispensable features of intelligent manufacturing systems (IMSs). A number of approaches have been described to apply different machine learning (ML) techniques for manufacturing problems, starting with rule induction in symbolic domains and pattern recognition techniques 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, the integration of neural and fuzzy techniques is treated and former solutions are analysed. A genetic algorithm (Gli) based approach is introduced to overcome problems that are experienced during manufacturing applications with other algorithms.