scholarly journals Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Zhan-bo Chen
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Least Squares ◽  
Performance Prediction ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Hydraulic Excavator ◽  
Simulation Results ◽  
Research On Application ◽  
The Mathematical Model

In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

2014 ◽  
Vol 1061-1062 ◽  
pp. 935-938
Author(s):  
Xin You Wang ◽  
Guo Fei Gao ◽  
Zhan Qu ◽  
Hai Feng Pu
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Vector Machines ◽  
Online Prediction ◽  
Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

scholarly journals Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Doddy Prayogo ◽  
Yudas Tadeus Teddy Susanto
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Hybrid Approach ◽  
Cohesive Soil ◽  
Training Data ◽  
Support Vector ◽  
Driven Piles ◽  
Self Tuning ◽  
Friction Capacity

This research presents a novel hybrid prediction technique, namely, self-tuning least squares support vector machine (ST-LSSVM), to accurately model the friction capacity of driven piles in cohesive soil. The hybrid approach uses LS-SVM as a supervised-learning-based predictor to build an accurate input-output relationship of the dataset and SOS method to optimize the σ and γ parameters of the LS-SVM. Evaluation and investigation of the ST-LSSVM were conducted on 45 training data and 20 testing data of driven pile load tests that were compiled from previous studies. The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The comparison showed that the ST-LSSVM performed better than LS-SVM, BPNN, and NN in terms of R, RMSE, and MAE. This comprehensive evaluation confirmed the capability of hybrid approach SOS and LS-SVM to modeling the accurate friction capacity of driven piles in clay. It makes for a reliable and robust assistance tool in helping all geotechnical engineers estimate friction pile capacity.

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

2018 ◽  
Vol 18 (3) ◽  
pp. 715-724 ◽  
Author(s):  
Xiao Li ◽  
Xin Liu ◽  
Clyde Zhengdao Li ◽  
Zhumin Hu ◽  
Geoffrey Qiping Shen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Data Processing ◽  
Least Squares ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Displacement Monitoring ◽  
Processing Efficiency ◽  
Foundation Pit ◽  
Vector Machines

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 566-569
Author(s):  
Qing Xin Zhang ◽  
Yong Tao ◽  
Zhan Bo Cui
Keyword(s):  
Support Vector Machine ◽  
Blast Furnace ◽  
Least Squares ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Furnace Temperature ◽  
Temperature Prediction ◽  
Data Description ◽  
Simulation Results

The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperatures change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.

scholarly journals Prediction for civil aero-engine performance after shop visit based on lazy support vector machine regression model

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Xuyun Fu ◽  
Xingjie Zhou ◽  
Shisheng Zhong
Keyword(s):  
Support Vector Machine ◽  
Performance Prediction ◽  
Prediction Accuracy ◽  
Engine Performance ◽  
Learning Ability ◽  
Model Complexity ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Support Vector Machine Regression ◽  
Aero Engine

Through consideration of problems that the influence of the aero-engine state before shop visit and the adopted maintenance work scope on its performance after shop visit is complex and the sample size is small, we propose a lazy support vector machine regression (LSVMR) model for aero-engine performance prediction after shop visit based on the ε-support vector machine regression (ε-SVMR) model. Unlike the ε-SVMR, the insensitive loss function in LSVMR depends on the distance between the training sample and the predicted sample. The proposed model not only makes full use of the information of the predicted sample, but also seeks the best tradeoff between the model complexity and the learning ability. In this article, we give the solving process of LSVMR and collect the actual aero-engine maintenance samples from an airline to validate it. By comparing the prediction accuracy among LSVMR, ε-SVMR and k-nearest neighbor algorithm (k-NN), we find that LSVMR has the best prediction accuracy and can be seen as an effective method for the aero-engine performance prediction after shop visit.

scholarly journals Developing A Mathematical Model for Planning Repetitive Construction Projects By Using Support Vector Machine Technique

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abbas M. Burhan ◽  
Kadhim Raheim Erzaij ◽  
Wadhah Amer Hatem
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Construction Projects ◽  
Absolute Error ◽  
Support Vector ◽  
Planning And Scheduling ◽  
Factors Affecting ◽  
Project Management System ◽  
Predicted Values ◽  
The Mathematical Model

Abstract Each project management system aims to complete the project within its identified objectives: budget, time, and quality. It is achieving the project within the defined deadline that required careful scheduling, that be attained early. Due to the nature of unique repetitive construction projects, time contingency and project uncertainty are necessary for accurate scheduling. It should be integrated and flexible to accommodate the changes without adversely affecting the construction project’s total completion time. Repetitive planning and scheduling methods are more effective and essential. However, they need continuous development because of the evolution of execution methods, essentially based on the repetitive construction projects’ composition of identical production units. This study develops a mathematical model to forecast repetitive construction projects using the Support Vector Machine (SVM) technique. The software (WEKA 3.9.1©2016) has been used in the process of developing the mathematical model. The number of factors affecting the planning and scheduling of the repetitive projects has been identified through a questionnaire that analyzed its results using SPSS V22 software. Three accuracy measurements, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were used to check the mathematical model and to compare the actual values with predicted values. The results showed that the SVM technique was more precise than those calculated by the conventional methods and was found the best generalization with R 97 %, MAE 3.6 %, and RMSE 7 %.

A New Control Method for Fermenter Temperature

2012 ◽  
Vol 236-237 ◽  
pp. 385-389
Author(s):  
Guang Hui Zeng ◽  
Yan Gan
Keyword(s):  
Support Vector Machine ◽  
Steady State ◽  
Least Squares ◽  
Predictive Control ◽  
Pid Control ◽  
Control Method ◽  
Support Vector ◽  
Control Performance ◽  
Simulation Results ◽  
Controlled Object

A new control method based on least squares support vector machine (LSSVM) and model predictive control (MPC) is proposed for the control of fermenter temperature. Existing PID control doesn’t consider the model of controlled object, so it tends to bring steady-state error. The proposed method utilizes LSSVM to obtain fermenter temperature’s model and then uses it to implement MPC. The simulation results show that our method has better control performance than traditional PID control

scholarly journals Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-26 ◽  
Author(s):  
Wei Xie ◽  
Jie-sheng Wang ◽  
Cheng Xing ◽  
Sha-Sha Guo ◽  
Meng-wei Guo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Soft Sensor ◽  
Support Vector ◽  
Structure Parameters ◽  
Generalization Ability ◽  
Sensor Model ◽  
Learning Machine

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

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