A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Mawloud Guermoui ◽  
Kacem Gairaa ◽  
John Boland ◽  
Toufik Arrif
Keyword(s):  
Support Vector Machine ◽  
Solar Radiation ◽  
Least Squares ◽  
Correlation Coefficient ◽  
Cuckoo Search ◽  
Measured Data ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bee Colony ◽  
Svm Model

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.

scholarly journals PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoyong Liu ◽  
Hui Fu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Svm Model ◽  
Two Stages ◽  
Best Parameters

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

scholarly journals A Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chai ◽  
Jiangze Du ◽  
Kin Keung Lai ◽  
Yan Pui Lee
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector Machine Model ◽  
Optimization Methods ◽  
Parameters Optimization ◽  
Least Square ◽  
Movement Direction ◽  
Support Vector ◽  
Machine Model ◽  
Mode Decomposition

This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including simplex, GS (grid search), PSO (particle swarm optimization), and GA (genetic algorithm). Experimental results show that the EMD-LSSVM model with GS algorithm outperforms other methods in predicting stock market movement direction.

scholarly journals Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

2018 ◽  
Vol 20 (4) ◽  
pp. 975-988 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi ◽  
Hamid R. Safavi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Standardized Precipitation Index ◽  
Cuckoo Search ◽  
Precipitation Index ◽  
Urmia Lake ◽  
Support Vector ◽  
Drought Forecasting ◽  
Svm Algorithm ◽  
Svm Model

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

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.

Toward Efficient Segmentation of Brain Tumors Based on Support Vector Machine Classifier Through Optimized RBF Kernel Parameters and Optimal Texture Features

2014 ◽  
Vol 8 (2) ◽  
pp. 15-33 ◽  
Author(s):  
Ahmed Kharrat ◽  
Mohamed Abid
Keyword(s):  
Support Vector Machine ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Tissue ◽  
Malignant Tumor ◽  
Benign Tumor ◽  
Parameters Optimization ◽  
Support Vector ◽  
Manual Segmentation ◽  
Svm Model

This paper presents a brain tumor automatic segmentation approach applied to magnetic resonance (MR) images. The authors' approach addresses all types of brain tumors. The proposed method involves therefore: image pre-processing, feature extraction via wavelet transform-spatial gray level dependence matrix (WT-SGLDM), dimensionality reduction using genetic algorithm (GA), parameters optimization by GA-SVM model and classification of the reduced features using support vector machine (SVM). These optimal features and optimized parameters are employed for the segmentation of brain tumor. The resulting method is aimed at early tumor diagnostics support by distinguishing between the brain tissue, benign tumor and malignant tumor tissue. The authors' contribution consists in involving the parameters optimization phase to improve the classification and segmentation results by using GA-SVM model. The segmentation results in different types of brain tissue are evaluated by comparison with the manual segmentation as well as with other existing techniques. The qualitative evaluation shows that their approach outperforms manual segmentation with a Match Percent measure (MP) equal to 97.08% and 98.89% for the malignant and the benign tumors respectively. The quantitative evaluation displays that the authors' attitude overtakes FCM algorithm with an accuracy rate of 99.69% for benign tumor and 99.36% for malignant tumor.

Prediction of Oxygen Consumption of BF in Iron & Steel Factories Based on Least Squares Support Vector Machine

2013 ◽  
Vol 634-638 ◽  
pp. 3143-3148
Author(s):  
Pei Lin Wu ◽  
Jian Jun Wang ◽  
Hong Juan Li ◽  
Hua Wang
Keyword(s):  
Support Vector Machine ◽  
Oxygen Consumption ◽  
Least Squares ◽  
Parameters Optimization ◽  
Support Vector ◽  
Production Data ◽  
Iron And Steel ◽  
Operation Speed ◽  
Oxygen System ◽  
Production Practice

In this paper, we presented a prediction model of oxygen consumption of blast furnace (BF) based on least squares support vector machine (LSSVM) with the production data of an iron and steel factory. This method utilizes data pre-processing and parameters optimization to improve the fitting precision and operation speed of the model. By comparing the prediction results using different models with actual production data, we found out that the modified regression model of LSSVM is more suitable to predict the trend of oxygen consumption than others. The prediction accuracy is satisfactory and is helpful for oxygen system dispatch and production practice.

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