Artificial neural network-based modeling of a gantry crane system/ Wahyudi ...[et al.]
In the process industry, the use of gantry crane systems for transporting payload is very common. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem, a feedba...
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| Format: | Book |
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2006.
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| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | repouitm_81737 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a ., Wahyudi |e author |
| 700 | 1 | 0 | |a Solihin, M.I. |e author |
| 700 | 1 | 0 | |a Albagul, A. |e author |
| 700 | 1 | 0 | |a Salami, M.J.E. |e author |
| 245 | 0 | 0 | |a Artificial neural network-based modeling of a gantry crane system/ Wahyudi ...[et al.] |
| 260 | |c 2006. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/81737/1/81737.PDF | ||
| 520 | |a In the process industry, the use of gantry crane systems for transporting payload is very common. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem, a feedback control system is introduced. To obtain high quality control, an accurate model of the crane model is highly needed. However, the linear model is often insufficient since the crane is characterized by nonlinearity. To overcome this problem, this paper introduces an application of artificiaI neural network to build the crane model including its nonlinearity. A multi layer feed forward neural network trained by using backpropagation learning algorithm has been adopted to develop the crane model. Simulation studies show the effectiveness of the proposed neural network to model the gantry crane system. | ||
| 546 | |a en | ||
| 690 | |a Construction equipment in building | ||
| 655 | 7 | |a Conference or Workshop Item |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/81737/ | |
| 856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/81737/ |z Link Metadata |