Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/61415
Title: Study on optimization of agent initial positions in land combat simulation
Authors: Wu, C.
Liang, Y.
Lee, H.P.
Lu, C.
Yang, X. 
Keywords: Genetic algorithm
Multi-agent
Radial basis function
Regression
Support vector machine
Issue Date: Mar-2004
Source: Wu, C.,Liang, Y.,Lee, H.P.,Lu, C.,Yang, X. (2004-03). Study on optimization of agent initial positions in land combat simulation. Progress in Natural Science 14 (3) : 257-261. ScholarBank@NUS Repository.
Abstract: The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that of using the traditional SVM with the same precision. We also summarize and present some experiences and trends on the optimization problem in land combat simulation.
Source Title: Progress in Natural Science
URI: http://scholarbank.nus.edu.sg/handle/10635/61415
ISSN: 10020071
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

25
checked on Dec 7, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.