Abstract
This paper investigates a fast parallel computing scheme for the leaning control of a class of two-layered Networked Learning Control Systems (NLCSs). This class of systems is subject to imperfect Quality of Service (QoS) in signal transmission, and requires a real-time fast learning. A parallel computational model for this task is established in the paper. Based on some of grid computing technologies and optimal scheduling, an effective scheme is developed to make full use of distributed computing resources, and thus to achieve a fast multi-objective optimization for the learning task under study. Experiments of the scheme show that it indeed provides a required fast on-line learning for NLCSs.
This work is supported by National Natural Science Foundation of China under Grant 60774059, the Excellent Discipline Head Plan Project of Shanghai under Grant 08XD14018, Shanghai Science and Technology International Cooperation Project 08160705900 and Mechatronics Engineering Innovation Group project from Shanghai Education Commission.
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Xu, L., Fei, M., Yang, T.C., Yu, W. (2010). Grids-Based Data Parallel Computing for Learning Optimization in a Networked Learning Control Systems. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_26
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DOI: https://doi.org/10.1007/978-3-642-15621-2_26
Publisher Name: Springer, Berlin, Heidelberg
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