scholarly journals Intelligent Prediction of the Construction Cost of Substation Projects Using Support Vector Machine Optimized by Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Tongyao Lin ◽  
Tao Yi ◽  
Chao Zhang ◽  
Jinpeng Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Project Management ◽  
Pso Algorithm ◽  
Construction Cost ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Power Substation ◽  
Cost Forecasting

To establish and consummate the electric power network, the construction and investment scale of power substation projects is expanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management. Accurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project management. However, affected by many factors, the construction cost of a power substation project usually presents strong nonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation project cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a particle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the PCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model parameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost prediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results show that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this model is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects.

Fault Diagnosis of PT Fuel System Based on Particle Swarm Optimization-Support Vector Machine

2011 ◽  
Vol 121-126 ◽  
pp. 2809-2813
Author(s):  
Dong Wang ◽  
Xin Qing Wang ◽  
Xiao Long Wang ◽  
Sheng Liang ◽  
Yang Zhao
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Fuel System ◽  
Swarm Optimization ◽  
Pressure Signal ◽  
Svm Model

In order to overcome the difficulty in selecting parameters of support vector machine (SVM) when modeling the PT fuel system fault diagnosis, SVM optimized by particle swarm optimization (PSO) algorithm was proposed. The PSO-SVM model was established and the fault multi-classifiers of the SVM were got. The pressure signal of the PT fuel inlet and outlet at different rotational speed and conditions was collected. The algorithm of PSO-SVM was used to train and recognize the pressure signal. The result of experiment confirms the validity of this method through comparison of the BP-NN, SVM and the PSO-SVM.

scholarly journals Forecasting Initial Public Offering Pricing Using Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) In Iran

2017 ◽  
Vol 7 (1) ◽  
pp. 336 ◽  
Author(s):  
Shaho Heidari Gandoman ◽  
Navab Kiamehr ◽  
Mahmood Hemetfar
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Initial Public Offering ◽  
Stock Exchange ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Benefits ◽  
Support Vector ◽  
Swarm Optimization ◽  
Public Offering

The present study compares the ability of neural networks, support vector machine, and model derived from combining particles swarm optimization (PSO) algorithm and support vector machine (SVM) to forecast the initial public offering pricing. The purpose of this research is to design a model that helps investors recognize the validity of the initial public offering pricing and hunt profitable opportunities. The variables used in this study are selected among those variables which are in the disposal of investors who have limited access to information before the offering. On the other hand, these results can be useful for publishing companies, admissions consultant, underwriting and legislators of the stock exchange. We have considered the ninth day offering prices, since volatilities are gone and prices seem to be more realistic. The results show that the combination of particle swarm optimization (PSO) algorithm and support vector machine (SVM) markedly increases the forecasting power. As a result, support vector machine models can increase the accuracy of initial public offering pricing and provide significant economic benefits as reducing less than real pricing costs.

Nonlinearity Compensation for Thermocouple Vacuum Gauge Using Particle Swarm Optimized Least Squares Support Vector Machine

2013 ◽  
Vol 321-324 ◽  
pp. 2177-2182
Author(s):  
Yao Geng Tang ◽  
Song Gao ◽  
Xing Qu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Support Vector ◽  
Vacuum Gauge ◽  
Swarm Optimization ◽  
Svm Model ◽  
Using Data ◽  
Nonlinearity Characteristic

A method for compensating nonlinear characteristic of thermocouple vacuum gauge is proposed. Least squares support vector machine (LS-SVM) is adopt as compensation model, of which parameters are optimized using particle swarm optimization (PSO) algorithm. Experimental results using data obtained on-site show that the proposed approach effectively compensates the nonlinearity characteristic, and the accuracy of this method is higher than those obtained by SVM model.

scholarly journals Research on the Prediction Model of Material Cost Based on Data Mining

2015 ◽  
Vol 9 (1) ◽  
pp. 1062-1066 ◽  
Author(s):  
Liu Shenyang ◽  
Gao Qi ◽  
Li Zhen ◽  
Li Si ◽  
Li Zhiwei
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Pso Algorithm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Total Cost ◽  
Material Cost ◽  
Relative Errors

Material cost prediction should be based on the scientific mathematical models so that the influence of subjective factors on the quota and other indicators of decomposition can be reduced. This paper analyzes the particle swarm optimization (PSO) algorithm to optimize the parameters of support vector machine and establishes the prediction model of material cost after preprocessing the actual data and uses the support vector regression (SVR) machine to carry out data mining. In the forecasting process, the total cost of material is first predicted and the predicted results are then adjusted with the actual value, and finally, the relative errors are tested. The result indicates that the forecasting effect is fulfilled.

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

2019 ◽  
Vol 7 (1) ◽  
pp. T97-T112 ◽  
Author(s):  
Zhi Zhong ◽  
Timothy R. Carr
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Kernel Functions ◽  
Oil Field ◽  
Support Vector ◽  
Well Log ◽  
Swarm Optimization ◽  
Svm Model

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Assessing the Freshness of Meat by Using Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

2013 ◽  
Vol 76 (11) ◽  
pp. 1916-1922
Author(s):  
XIAO GUAN ◽  
JING LIU ◽  
QINGRONG HUANG ◽  
JINGJUN LI
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Ph Value ◽  
Optimal Parameter ◽  
Particle Swarm ◽  
Plate Count ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Aerobic Plate Count

To improve the performance of meat freshness identification systems, we present a new identification method based on quantum-behaved particle swarm optimization (QPSO) and the support vector machine (SVM). Fresh pork, beef, mutton, and shrimp samples were stored in a hypobaric chamber for several days, and the conventional indices of meat freshness, including total volatile basic nitrogen content, aerobic plate count, pH value, and sensory scores, were determined to achieve the identification of sample freshness. However, the experiments showed that it was difficult to obtain an ideal freshness assessment by any single physicochemical or sensory property. Therefore, SVM was introduced to use these data to build a freshness model. Furthermore, QPSO was proposed to seek the optimal parameter combination of SVM. The experimental results indicated that the hybrid SVM model with QPSO could be used to predict meat freshness with 100% classification accuracy.

scholarly journals A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fanping Zhang ◽  
Huichao Dai ◽  
Deshan Tang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Swarm Optimization ◽  
Svm Model ◽  
Monthly Streamflow

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

PSO-SVM Model Based Prediction and Analysis for the Formation of Navigation Channel Silt

2014 ◽  
Vol 543-547 ◽  
pp. 4133-4136
Author(s):  
Ping Wang ◽  
Zhi Hong Qie ◽  
Fu Sheng Yang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Deterministic Model ◽  
Deformation Monitoring ◽  
Small Sample ◽  
Support Vector ◽  
Swarm Optimization ◽  
Navigation Channel ◽  
Svm Model ◽  
Selection Of

Monitor and predict the change of navigation channel silt is important to ensure the safety of the channel while one of many difficulties is the deformation monitoring data is complicated and nonlinear, so its difficult to establish a deterministic model. Supporting vector machine could be widely used in the prediction of the formation of navigation channel silt because it has a good generalized ability, which could solve the problems like small sample, nonlinear, high-dimension. Because whether the algorithm could work or not based on the selection of the parameters, so a PSO-SVM based prediction model of the formation of the silt was established by using particle swarm optimization, which is a the fast overall optimization, and then was used to optimize the model parameter of the support vector machine. Study shows utilize this model in the silt formation in Huanghua harbor is plausible.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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