Grids-based data parallel computing for learning optimization in a networked learning control systems

Authors:
Lijun Xu;Minrui Fei;T. C. Yang;Wei Yu
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, China;University of Sussex, UK;CSK Systems (Shanghai) Co., LTD., Shanghai, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Year:
2010

Citing 5
Cited 0

Integrated genetic algorithm and goal programming for network topology design problem with multiple objectives and multiple criteria

IEEE/ACM Transactions on Networking (TON)
An Open Grid Services Architecture Primer

Computer
A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem

Operations Research
Adaptive tracking of network behavioral signals for real time forensic analysis of service quality degradation

IEEE Transactions on Network and Service Management
Quantum Genetic Optimization

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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.