Improved Simulation of Double Integrals Based on Monte-Carlo Method

Hong Tao Wang; Man Zhi Li; You Jian Shen

doi:10.4028/www.scientific.net/AMR.655-657.1016

Paper Titles

The Template Matching Research Based on Multiple Images Fusion
p.993

An Improving Algorithm Based on SOM Clustering and its Applications
p.1000

Multi-Dimensional Global Approximation Method Based Improved MARS
p.1005

Out-of-Core Incremental Algorithm for 2D Laguerre Diagram and Visualization Technique
p.1009

Improved Simulation of Double Integrals Based on Monte-Carlo Method
p.1016

An Approach of Robotic Kinematics Parameters Calibration
p.1023

Design and Dynamic Analysis of Mechanical Fingers Based on ProE
p.1029

Design and Analysis of a Hip Rehabilitation Mechanism
p.1033

Design and Analysis of a Mechanism of Ankle Rehabilitation Robot
p.1038

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 655-657Improved Simulation of Double Integrals Based on...

Improved Simulation of Double Integrals Based on Monte-Carlo Method

Abstract:

The traditional method of solving double integral by Monte Carlo method is limited with the integral area, it can only be solved the double integral on the rectangular area. To address this limitation, based on the average method of Monte Carlo method,which puts forward that the uniform distribution should be combined with area of the integral region to improve algorithm, the special rectangle integral region is extended to the general integral region.In this way the simulation accuracy and computational efficiency is improved. Practical example shows that improved algorithm simplifies the calculation process, effectively reduces the computational difficulty, Improves the simulation accuracy and computational efficiency.The procedure is simple and easy to debug. The double integral calculation method is simple and effective, so this improved algorithm is more practical.

You might also be interested in these eBooks

Engineering Solutions for Manufacturing Processes

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 655-657)

Pages:

1016-1019

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.655-657.1016

Citation:

Cite this paper

Online since:

January 2013

Authors:

Hong Tao Wang, Man Zhi Li, You Jian Shen

Keywords:

Double Integral, Monte-Carlo Method (MC-Method), Simulation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Fishman, G S. Monte Carlo-Concepts, Algorithms and Applications[M]. NewYork: Springer, (1996).

Google Scholar

[2] Niederreiter,H. Random Number Generation and Quasi-Monte Carlo methods[M]. CBMS-NSF Regional Conference Series in Applied Mathematics, No. 63, SIAM. (1992).

DOI: 10.1002/bimj.4710350415

Google Scholar

[3] Robert C.P. and Casella,G. Monte Carlo Statistical Method(secondedition) [M]. New York: Springer, (2004).

Google Scholar

[4] Miguel A. Usabel. Applications to risk theory of a Monte-Carlo multiple integration method[J]. Mathematics and Economics 23(1998)71-83.

DOI: 10.1016/s0167-6687(98)00026-2

Google Scholar

[5] Jun S Liu. Monte Carlo Strategies in Scientific Computing[M]. SpringerSeries in Statistics, 2005: 53-77.

Google Scholar

[6] Sun Weijun, Qin Hua. Application of triple integral based on Monte Carlo method [J]. Journal of Shandong University of Technology(Natural Science Edition), 2008, 22(1): 60-63.

Google Scholar