Prediction of Smooth Hysteretic Model Parameters Using Support Vector Regression

2021 ◽  
Author(s):  
Tzu-Kang Lin ◽  
Tzu-Hui Yang ◽  
Ping-Hsiung Wang ◽  
Rong-Chung Zeng ◽  
Kuo-Chun Chang
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Model Parameters ◽  
Hysteretic Model

scholarly journals Fault Isolation for Nonlinear Systems Using Flexible Support Vector Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Yufang Liu ◽  
Bin Jiang ◽  
Hui Yi ◽  
Cuimei Bo
Keyword(s):  
Support Vector Regression ◽  
Fault Isolation ◽  
Support Vector ◽  
Model Parameters ◽  
Sample Distribution ◽  
Flexible Support ◽  
Parameters Selection ◽  
Residual Generator ◽  
Frequency Power ◽  
Selection Of

While support vector regression is widely used as both a function approximating tool and a residual generator for nonlinear system fault isolation, a drawback for this method is the freedom in selecting model parameters. Moreover, for samples with discordant distributing complexities, the selection of reasonable parameters is even impossible. To alleviate this problem we introduce the method of flexible support vector regression (F-SVR), which is especially suited for modelling complicated sample distributions, as it is free from parameters selection. Reasonable parameters for F-SVR are automatically generated given a sample distribution. Lastly, we apply this method in the analysis of the fault isolation of high frequency power supplies, where satisfactory results have been obtained.

scholarly journals Field data–driven online prediction model for icing load on power transmission lines

2019 ◽  
Vol 53 (1-2) ◽  
pp. 126-140
Author(s):  
Yong Chen ◽  
Peng Li ◽  
Wenping Ren ◽  
Xin Shen ◽  
Min Cao
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Transmission Lines ◽  
Prediction Accuracy ◽  
Field Data ◽  
Power Transmission ◽  
Data Driven ◽  
Support Vector ◽  
Model Parameters ◽  
Online Prediction

Methods for the accurate prediction of icing loads in overhead transmission lines have become an important research topic for electrical power systems as they are necessary for ensuring the safety and stability of power-grid operations. Current machine learning models for the prediction of icing loads on transmission lines are afflicted by the following issues: insufficient prediction accuracy, high randomity in the selection of kernel functions and model parameters, and a lack of generalizability. To address these issues, we propose a field data–driven online prediction model for icing loads on transmission lines. First, the effects of the type of kernel function used in the support vector regression algorithm on the prediction accuracy of the model were analyzed using micrometeorological data and icing data collected by on-site monitoring systems. The particle swarm optimization algorithm was then used to optimize and determine the model parameters such as penalty coefficients. An offline support vector regression prediction model was thus constructed. Using the accurate online support vector regression algorithm, the weighting coefficients of the samples were dynamically adjusted to satisfy the Karush–Kuhn–Tucker conditions, which allowed online updates to be made to the regression function and prediction model. Finally, a simulation analysis was performed using actual icing incidents that occurred in a transmission line of the Yunnan Power Grid, which demonstrated that our model can make online predictions for the icing load on transmission lines in actual applications. Our model proved to be superior to conventional icing-load prediction models with regard to the single-step and multi-step prediction accuracies and generalizability. Hence, our prediction model will improve the decision-making processes regarding the deicing and maintenance of power transmission and transformation systems.

scholarly journals Predicting Axial Impairment in Parkinson’s Disease through a Single Inertial Sensor

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 412
Author(s):  
Luigi Borzì ◽  
Ivan Mazzetta ◽  
Alessandro Zampogna ◽  
Antonio Suppa ◽  
Fernanda Irrera ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Regression Model ◽  
Support Vector Regression ◽  
Regression Models ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Support Vector ◽  
Model Parameters ◽  
The Impact

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

scholarly journals Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

2021 ◽  
Vol 22 ◽  
pp. 29
Author(s):  
Jianxiong Kang ◽  
Yanjun Lu ◽  
Hongbo Luo ◽  
Jie Li ◽  
Yutao Hou ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Internal Combustion Engines ◽  
Piston Ring ◽  
Cylinder Liner ◽  
Internal Combustion ◽  
Assessment Model ◽  
Support Vector ◽  
Model Parameters ◽  
Fuzzy Uncertainty ◽  
Proposed Model

The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry
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