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.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

scholarly journals A 4D Trajectory Prediction Model Based on the BP Neural Network

2019 ◽  
Vol 29 (1) ◽  
pp. 1545-1557 ◽  
Author(s):  
Zhi-Jun Wu ◽  
Shan Tian ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Spline Interpolation ◽  
Absolute Error ◽  
Maximum Absolute Error ◽  
Trajectory Prediction ◽  
Model Based ◽  
Crossing Point

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

Life Prediction for Silicon Pressure Sensor Based on SVR

2012 ◽  
Vol 187 ◽  
pp. 241-244
Author(s):  
Zhi Hua Zhai ◽  
Ping Li Wu
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Pressure Sensor ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Grid Method ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Training Support

In order to improve the reliability of silicon pressure sensor, life prediction for silicon pressure sensor should be performed. Life prediction for silicon pressure sensor based on support vector regression is proposed in the paper. Grid method is used to determine the parameters of support vector regression in the process of training support vector regression model. Life for silicon pressure sensor under the conditions of different pressures is given in the experimental analysis. The comparison of the errors and mean errors between support vector regression and BP neural network indicates that life prediction accuracy of support vector regression for silicon pressure sensor is higher than that of BP neural network.

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
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