Real-time identification of the draft system using neural network |
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Authors: | Soon Yong Chun Han Jo Bae Seon Mi Kim Moon W Suh P Grady Won Seok Lyoo Won Sik Yoon Sung Soo Han |
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Institution: | 1. School of Textiles, Yeungnam University, 712-749, Gyeongsan, Korea 2. School of IT Electronic Engineering, Dongyang University, 750-711, Yeongju, Korea 3. College of Textiles, North Carolina State University, 27695, Raleigh, NC, USA
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Abstract: | 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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