scholarly journals Application of support vector machine model in wind power prediction based on particle swarm optimization

2015 ◽  
Vol 8 (6) ◽  
pp. 1267-1276 ◽  
Author(s):  
Ning Lu ◽  
◽  
Ying Liu ◽  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Power Prediction ◽  
Machine Model ◽  
Swarm Optimization ◽  
Wind Power Prediction

scholarly journals A novel deformation forecasting method utilizing comprehensive observation data

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879633 ◽  
Author(s):  
Sunwen Du ◽  
Yao Li
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Mean Square Error ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Mean Square ◽  
Machine Model ◽  
Swarm Optimization ◽  
Forecasting Method

Mine disasters often happen unpredictably and it is necessary to find an effective deformation forecasting method. A model between deformation data and the factors data that affected deformation is built in this study. The factors contain hydro-geological factors and meteorological factors. Their relationship presents a complex nonlinear relationship which cannot be solved by ordinary methods such as multiple linear regression. With the development of artificial intelligence algorithm, Artificial Neural Network, Support Vector Machine, and Extreme Learning Machine come to the fore. Support Vector Machine could establish a deformation prediction model perfectly in the condition that there is less input data and output data. The deformation forecast model that uses quantum-behaved particle swarm optimization algorithm is selected to optimize the Support Vector Machine. The optimum configuration of Support Vector Machine model needs to be determined by two parameters, that is, normalized mean square error and correlation coefficient (R). Quantum-behaved particle swarm optimization could determine the optimal parameter values by minimizing normalized mean square error. It investigates the application effect of the proposed quantum-behaved particle swarm optimization–Support Vector Machine model by comparing their performances of popular forecasting models, such as Support Vector Machine, GA-Support Vector Machine, and particle swarm optimization–Support Vector Machine models. The results show that the proposed model has better performances in mine slope surface deformation and is superior to its rivals.

scholarly journals A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Salam Allawi Hussein ◽  
Alyaa Abduljawad Mahmood ◽  
Mohammed Iqbal Dohan
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Support Vector Machine Model ◽  
Particle Swarm ◽  
Support Vector ◽  
Machine Model ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Proposed Model

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

scholarly journals Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 512 ◽  
Author(s):  
Chao Fu ◽  
Guo-Quan Li ◽  
Kuo-Ping Lin ◽  
Hui-Juan Zhang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Wind Power ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction ◽  
Svm Model

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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