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.

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 Automatic Task Classification via Support Vector Machine and Crowdsourcing

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hyungsik Shin ◽  
Jeongyeup Paek
Keyword(s):  
Support Vector Machine ◽  
Mobile Devices ◽  
Prediction Accuracy ◽  
Training Data ◽  
Amazon Mechanical Turk ◽  
Support Vector ◽  
Data Set ◽  
English Sentence ◽  
Task Classification ◽  
Personal Assistant

Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

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.

A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine

2019 ◽  
Vol 41 (15) ◽  
pp. 4436-4449 ◽  
Author(s):  
Wenlong Fu ◽  
Kai Wang ◽  
Chu Zhang ◽  
Jiawen Tan
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Hybrid Approach ◽  
Model Performance ◽  
Vibration Signal ◽  
Support Vector ◽  
Optimal Parameters ◽  
Algorithm Optimization ◽  
Series Analysis ◽  
Hydroelectric Unit

Accurate vibrational trend measuring for hydroelectric unit (HEU) is of great significance for safe and economic operation of unit. For this purpose, a novel hybrid approach based on variational mode decomposition (VMD), singular value decomposition (SVD)-based phase space reconstruction (PSR) and least squares support vector machine (LSSVM) improved with adaptive sine cosine algorithm optimization (ASCA) is proposed. Firstly, the raw vibration signal is preprocessed into several components with different scales by VMD, while the residual of VMD is defined as an additional component. Then, SVD with median filtering is utilized to unearth the dominating characteristic ingredients of each component, with which the chaotic series analysis will be effectively implemented. Moreover, the optimal parameters of PSR for each original component are determined by applying grid search on the corresponding dominating component. Later, LSSVM improved by ASCA are established for all the components, whose inputs and outputs are obtained by performing PSR with the optimal parameters. Finally, the measuring results of vibration trend are deduced by accumulating the prediction values of all the components. Furthermore, five related methods are employed to evaluate the effectiveness of the proposed approach. The results illustrate that: (1) the VMD-based models obtained better evaluation indexes compared with the relevant models through significantly weakening the non-stationarity of the original signal; (2) the proposed SVD-based PSR enhanced efficiency of chaotic system restoration, thus to improve the measuring accuracy effectively; (3) the proposed ASCA optimization algorithm could effectively search the parameters of LSSCVM, which contributes to improving model performance.

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.

DIESEL ENGINE MODELLING USING EXTREME LEARNING MACHINE UNDER SCARCE AND EXPONENTIAL DATA SETS

2013 ◽  
Vol 21 (supp02) ◽  
pp. 87-98 ◽  
Author(s):  
PAK KIN WONG ◽  
CHI MAN VONG ◽  
CHUN SHUN CHEUNG ◽  
KA IN WONG
Keyword(s):  
Support Vector Machine ◽  
Diesel Engine ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Logarithmic Transformation ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
Sample Data ◽  
Learning Machine

To predict the performance of a diesel engine, current practice relies on the use of black-box identification where numerous experiments must be carried out in order to obtain numerical values for model training. Although many diesel engine models based on artificial neural networks (ANNs) have already been developed, they have many drawbacks such as local minima, user burden on selection of optimal network structure, large training data size and poor generalization performance, making themselves difficult to be put into practice. This paper proposes to use extreme learning machine (ELM), which can overcome most of the aforementioned drawbacks, to model the emission characteristics and the brake-specific fuel consumption of the diesel engine under scarce and exponential sample data sets. The resulting ELM model is compared with those developed using popular ANNs such as radial basis function neural network (RBFNN) and advanced techniques such as support vector machine (SVM) and its variants, namely least squares support vector machine (LS-SVM) and relevance vector machine (RVM). Furthermore, some emission outputs of diesel engines suffer from the problem of exponentiality (i.e., the output y grows up exponentially along input x) that will deteriorate the prediction accuracy. A logarithmic transformation is therefore applied to preprocess and post-process the sample data sets in order to improve the prediction accuracy of the model. Evaluation results show that ELM with the logarithmic transformation is better than SVM, LS-SVM, RVM and RBFNN with/without the logarithmic transformation, regardless the model accuracy and training time.

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.

Prediction of friction capacity of driven piles in clay using the support vector machine

2008 ◽  
Vol 45 (2) ◽  
pp. 288-295 ◽  
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Model Complexity ◽  
Support Vector ◽  
Ann Model ◽  
Driven Piles ◽  
Learning Technique ◽  
Practical Tool ◽  
Artificial Neural Network Ann ◽  
Friction Capacity ◽  
Better Than

The support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy (ε) insensitive loss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.

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