Study on Dynamic Load Balance Method Based on Genetic Algorithm and RBF Neural Network-Reference-Cited by-同舟云学术

Study on Dynamic Load Balance Method Based on Genetic Algorithm and RBF Neural Network

Published:2010-05 Issue: Volume:108-111 Page:207-210
ISSN:1662-8985
Container-title:Advanced Materials Research
language:
Short-container-title:AMR

Author:

Zeng Qing Wei¹,Xu Zhi Hai¹,Deng Geng Sheng¹

Affiliation:

1. Nanchang University

Abstract

Dynamic load balance is the critical pivotal role of network parallel and distributed computing. In order to solve the drawbacks of BP neural network, RBF neural network(RBFNN) is applied to dynamic load balance of network. And genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network, the method is well suited for searching global optimal values. In the paper, genetic algorithm and RBF neural network (GA-RBFNN) is adopted to dynamic load balance of network. The cases are applied to study the ability of dynamic load balance. The experimental results indicate that GA- RBF neural network is better dynamic load balance method than BP neural network.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Link

https://www.scientific.net/AMR.108-111.207.pdf

Reference6 articles.

1. LIU Zhu-song, LI Zhen-kun, YE Zhi-ping, Research on Dynamic Load Balance Algorithm Based on Bayes Theory, Modern Computer, 2007, no. 3, pp.12-14.

2. ZHANG Xian-zhe, YANG Yang, MA Xiao, A Strategy of Dynamic Load Balancing based on Real-time Load, Computer Knowledge and Technology, 2009, vol. 5, no. 1, pp.199-201.

3. ZHAO Li, CHENG Rong, A Dynamic Load Balancing Scheme for Parallel Back-Propagation Neural Networks Algorithm, Computer Technology and Development, 2006, vol. 16, no. 7, pp.67-69.

4. U. Becciani, R. Ansaloni, V. Antonuccio-Delogu, G. Erbacci, M. Gambera, A. Pagliaro, A parallel tree code for large N-body simulation: dynamic load balance and data distribution on a CRAY T3D system, Computer Physics Communications, 1997, vol. 106, no. 1-2, pp.105-113.

5. Juan Ignacio Mulero-Martínez, Analysis of the errors in the modelling of manipulators with Gaussian RBF neural networks, Neurocomputing, 2009, vol. 72, no. 7-9, p.1969-(1978).