Optimal Parameter Estimation for Muskingum Model Using a Modified Particle Swarm Algorithm

2010 ◽  
Author(s):  
Wenchuan Wang ◽  
Yingbin Kang ◽  
Lin Qiu
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Muskingum Model ◽  
Optimal Parameter Estimation ◽  
Swarm Algorithm

scholarly journals Optimal Parameter Estimation for Muskingum Model Using a CSS-PSO Method

2013 ◽  
Vol 5 ◽  
pp. 480954 ◽  
Author(s):  
S. Talatahari ◽  
R. Sheikholeslami ◽  
B. Farahmand Azar ◽  
H. Daneshpajouh
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Muskingum Model ◽  
Optimal Parameter Estimation

Parameter estimation of S-shaped growth model: A modified particle swarm algorithm

2012 ◽  
Vol 17 (2) ◽  
pp. 137-143
Author(s):  
Xing Xu ◽  
Bo Wei ◽  
Yu Wu ◽  
Bingxiang Liu ◽  
Yuanxiang Li
Keyword(s):  
Parameter Estimation ◽  
Growth Model ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Algorithm

GPU-Based PSO Application in Multiuser Detection and Trajectory Parameter Estimation

2013 ◽  
Vol 340 ◽  
pp. 829-832
Author(s):  
Lei Sun ◽  
Han Tao Zhang ◽  
Xiao Ping Zhou
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Capacity ◽  
Particle Swarm Algorithm ◽  
Processing Unit ◽  
Trajectory Parameter ◽  
Multiuser Detector ◽  
Swarm Algorithm

The parallel character of particle swarm algorithm (PSO) and the Graphic Processing Unit (GPU) technology of Compute United Device Architecture (CUDA) from NVIDIA are analyzed. Two methods of the realization of PSO based on GPU are discussed. One method is using the module of open source particle swarm algorithm supporting the GPU, with the application of multiuser detector (MUD). The other method is using the module of MATLAB supporting the GPU with the application of the moving parameter estimation. The test results show that the PSO algorithm based on GPU technology can significantly improve the speed of system capacity, to solve the problem of multi-dimensional global optimization, with the poor real-time performance. It can be widely used in the project of high real-time requirements.

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