In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classificat...In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.展开更多
With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting m...With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.展开更多
Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet hig...Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.展开更多
文摘In the classification problem,deep kernel extreme learning machine(DKELM)has the characteristics of efficient processing and superior performance,but its parameters optimization is difficult.To improve the classification accuracy of DKELM,a DKELM algorithm optimized by the improved sparrow search algorithm(ISSA),named as ISSA-DKELM,is proposed in this paper.Aiming at the parameter selection problem of DKELM,the DKELM classifier is constructed by using the optimal parameters obtained by ISSA optimization.In order to make up for the shortcomings of the basic sparrow search algorithm(SSA),the chaotic transformation is first applied to initialize the sparrow position.Then,the position of the discoverer sparrow population is dynamically adjusted.A learning operator in the teaching-learning-based algorithm is fused to improve the position update operation of the joiners.Finally,the Gaussian mutation strategy is added in the later iteration of the algorithm to make the sparrow jump out of local optimum.The experimental results show that the proposed DKELM classifier is feasible and effective,and compared with other classification algorithms,the proposed DKELM algorithm aciheves better test accuracy.
基金funded by Liaoning Provincial Department of Science and Technology(2023JH2/101600058)。
文摘With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.
基金National Natural Science Foundation of China(Grant No.52309050,32372680)Youth Backbone Teacher Project of Henan University of Science and Technology(Grant No.13450013 and 3450010)+1 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.24B416001)Innovative Research Team(Science and Technology)in the University of Henan Province(Grant No.23IRTSTHN024).
文摘Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.
