MULTISTRATEGY LEARNING APPROACHES TO GENERATE AND TUNE FUZZY CONTROL-STRUCTURES AND THEIR APPLICATION IN MANUFACTURING

Intelligence is strongly connected with learning adapting abilities, t herefore 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 fo r manufacturing problems, starting with rule induction in symbolic dom ains and pattern recognition techniques in numerical, subsymbolic doma ins. In recent years, artificial neural network (ANN) based learning i s the dominant ML technique in manufacturing. However, mainly because of the 'black box' nature of ANNs, these solutions have limited indust rial acceptance. In the paper, the integration of neural and fuzzy tec hniques is treated and former solutions are analysed. A genetic algori thm (Gli) based approach is introduced to overcome problems that are e xperienced during manufacturing applications with other algorithms.