Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Data exchange standards for simulation: integrating capacity simulation into process planning
Proceedings of the 33nd conference on Winter simulation
Adaptive Logic Networks for Facial Feature Detection
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
Special issue on integrated and hybrid intelligent systems in product design and development
International Journal of Knowledge-based and Intelligent Engineering Systems - Integrated and hybrid intelligent systems in product design and development
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Production lines are often planned with very demanding time constraints. The offer phase commonly lasts only few days. It comprises conceptual work, layout planning, calculation and writing of the offer. Today, elaborated tools like material-flow simulation, which analyze the layout's quality, are rarely used in the offer phase, because they require much modeling time. Others are not applied due to strong restrictions or high abstraction level. In this paper, a neuro-fuzzy based approximation approach to forecast material-flow behavior in the offer phase is proposed. Within this approach, a neuro-fuzzy system maps material-flow simulation input parameters on performance measures. It is trained with results of multiple simulations using a model of the production line. Four different function approximation systems - CANFIS, NefPROX, Fuzzy Graph Construction and Adaptive Logic Networks - are tested. Results are compared to simple analytic forecast tools that are common in industry. The approximation approach yields results comparable to the analytic approach. Furthermore, it is able to learn behavior of production lines, which does not follow the analytic approach's model restrictions.