The cascade-correlation learning architecture
Advances in neural information processing systems 2
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
A genetic procedure for RBF neural networks centers selection
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Job-shop-systems: continuous modeling and impact of external dynamics
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
A framework for the definition and generation of artificial neural networks
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Wind speed prediction using artificial neural networks
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
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In order to keep up with the fast changing requirements of today's global markets, production planning and control systems need a continuous advancement. In recent years, methods from the field of artificial intelligence, such as biologically inspired algorithms, software agents or artificial neural networks, have proven their innovation potential in tasks related to production planning and control. However, the practical application is often difficult due to the lack of experience with these new approaches. This paper deals with the use of artificial neural networks as control methods in a shop floor environment. It provides an evaluation of three common network architectures based on material flow simulations with a generic shop floor model.