document: Design and implementation of a flexible manufacturing control system using neural network Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented . The recurrent neural network -LRB- RNN -RRB- type is used for such a purpose . Contrary to the programmable controller , an RNN-based sequential controller is based on a definite mathematical model rather than depending on the experience and trial and error techniques . The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory . Adequate guidelines of how to construct an RNN-based sequential controller are presented . These guidelines are applied to different case studies . The proposed controller is tested by simulations and real-time experiments . These tests prove the successfulness of the proposed controller performances . Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman 's RNN can be effective in designing a sequential controller for event-based type manufacturing systems . In addition , the simulation results assure the effectiveness of the proposed controller to overcome the effect of noisy inputs
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I
extractive_keyphrases: finite state automata noisy inputs programmable controller recurrent neural network
abstractive_keyphrases: elman network flexible manufacturing systems ladder language pneumatic system sequential control learning fms