scholarly journals Gas detonation cell width prediction model based on support vector regression

2017 ◽  
Vol 49 (7) ◽  
pp. 1423-1430 ◽  
Author(s):  
Jiyang Yu ◽  
Bingxu Hou ◽  
Alexander Lelyakin ◽  
Zhanjie Xu ◽  
Thomas Jordan
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Cell Width ◽  
Detonation Cell ◽  
Gas Detonation ◽  
Model Based

scholarly journals Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)

2019 ◽  
Vol 9 (15) ◽  
pp. 2983 ◽  
Author(s):  
Jiao Liu ◽  
Guoyou Shi ◽  
Kaige Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Identification System ◽  
Trajectory Prediction ◽  
Model Based

There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient.

Application of Combined Prediction Model Based on Core and Coritivity Theory in Continuous Blood Pressure Prediction

2021 ◽  
Vol 24 ◽  
Author(s):  
Kai Zhou ◽  
Zhixiang Yin ◽  
Fei Guo ◽  
Jiasi Li
Keyword(s):  
Blood Pressure ◽  
Random Forest ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Estimation Error ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Random Forest Regression ◽  
Support Vector Regression Model ◽  
Model Based

Background and Objective: Blood pressure is vital evidence for clinicians to predict diseases and check the curative effect of diagnosis and treatment. To further improve the prediction accuracy of blood pressure, this paper proposes a combined prediction model of blood pressure based on coritivity theory and photoplethysmography. Method: First of all, we extract eight features of photoplethysmogram, followed by using eight machine learning prediction algorithms such as K-nearest neighbor, classification and regression trees and random forest to predict systolic blood pressure. Secondly, aiming at the problem of sub-model selection of combination forecasting model, from the point of graph theory, we construct an undirected network graph G, the results of each single prediction model constitute a vertex set. If the maximum mutual information coefficient between vertices is greater than or equal to 0.69, the vertices are connected by edges. The maximum core of graph G is a submodel of the combinatorial model. Results: According to the definition of core and coritivity, the maximum core of G is random forest regression and Gaussian kernel support vector regression model. The results show that the SDP estimation error of the combined prediction model based on random forest regression and Gaussian kernel support vector regression is 3.56 ±5.28mmhg, which is better than other single models and meets the AAMI standards. Conclusion: The combined model determined by core and coritivity has higher prediction performance for blood pressure.

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

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