Real-time Identification of the Draft System Using Neural Network
Making a good model is one of the most important aspects in the field of a control system. If one makes a good model, one is now ready to make a good controller for the system. The focus of this thesis lies on system modeling, the draft system in specific. In modeling for a draft system, one of the...
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| Veröffentlicht in: | Fibers and polymers 2006-03, Vol.7 (1), p.62-65 |
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| container_title | Fibers and polymers |
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| creator | Soon Yong Chun Han Jo Bae Seon Mi Kim Moon W. Suh P. Grady Won Seok Lyoo Won Sik Yoon Sung Soo Han |
| description | Making a good model is one of the most important aspects in the field of a control system. If one makes a good model, one is now ready to make a good controller for the system. The focus of this thesis lies on system modeling, the draft system in specific. In modeling for a draft system, one of the most common methods is the 'least-square method'; however, this method can only be applied to linear systems. For this reason, the draft system, which is non-linear and a time-varying system, needs a new method. This thesis proposes a new method (the MLS method) and demonstrates a possible way of modeling even though a system has input noise and system noise. This thesis proved the adaptability and convergence of the MLS method. |
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| ispartof | Fibers and polymers, 2006-03, Vol.7 (1), p.62-65 |
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| subjects | Control Draft system Modeling Neural network Sliver |
| title | Real-time Identification of the Draft System Using Neural Network |
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