Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the ...Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.展开更多
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d...The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.展开更多
A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to ...A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.展开更多
轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵...轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵为目标函数,用INFO优化算法替代牛顿迭代法,解决了牛顿迭代法初始参数易设置不当以及容易陷入局部最优的问题。仿真结果对比表明,在不同分辨率带宽、不同信干比等条件下,INFO-BSS的检测性能都要优于常规算法。展开更多
基金This research is funded by Prince Sattam BinAbdulaziz University,Grant Number IF-PSAU-2021/01/18921.
文摘Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.
基金supported by the National Research and Development Project of China (2020YFB2008400).
文摘The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.
基金Supported by the joint fund of National Natural Science Foundation of China and Civil Aviation Administration Foundation of China(No.U1233201)
文摘A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.
文摘轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵为目标函数,用INFO优化算法替代牛顿迭代法,解决了牛顿迭代法初始参数易设置不当以及容易陷入局部最优的问题。仿真结果对比表明,在不同分辨率带宽、不同信干比等条件下,INFO-BSS的检测性能都要优于常规算法。
