Grids-Based Data Parallel Computing for Learning Optimization in a Networked Learning Control Systems

摘要：

This paper investigates a fast parallel computing scheme for the leaning control of a class of twolayered 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.

关键词： Grids Parallel computing Optimization Real-time

作者: Lijun Xu Minrui Fei T.C.Yang Wei Yu

作者单位: Shanghai Key Laboratory of Power Station Automation Technology,Shanghai University, Shanghai 200072, University of Sussex, UK CSK Systems(Shanghai) Co., LTD, Shanghai 200002, China

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 221-232

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）