A machine learning approach for the support of preliminary structural design

This paper deals with the representation and acquisition of structural design knowledge using fuzzy systems. A new approach for linguistic fuzzy modeling as well as a multi-objective evolutionary algorithm for the data-driven design of fuzzy systems is presented. The developed genetic fuzzy system has been applied to test problems and real-world tasks. Making use of the proposed approaches the interpretability of fuzzy systems can be increased without loss of accuracy. The developed system facilitates the knowledge acquisition and improves the maintainability of the knowledge base.