TY - JOUR
A2 - Gasparetto, Alessandro
AU - Yang, Xue-cun
AU - Yan, Xiao-ru
AU - Song, Chun-feng
PY - 2015
DA - 2015/08/10
TI - Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine
SP - 542182
VL - 2015
AB - For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.
SN - 1024-123X
UR - https://doi.org/10.1155/2015/542182
DO - 10.1155/2015/542182
JF - Mathematical Problems in Engineering
PB - Hindawi Publishing Corporation
KW -
ER -