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
Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selecti...Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.展开更多
According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision ma...According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R^2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.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.
文摘Extreme learning machine(ELM)allows for fast learning and better generalization performance than conventional gradient-based learning.However,the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability.Further,choosing the optimal number of hidden nodes for a network usually requires intensive human intervention,which may lead to an ill-conditioned situation.In this context,chemical reaction optimization(CRO)is a meta-heuristic paradigm with increased success in a large number of application areas.It is characterized by faster convergence capability and requires fewer tunable parameters.This study develops a learning framework combining the advantages of ELM and CRO,called extreme learning with chemical reaction optimization(ELCRO).ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy.We evaluate its performance by predicting the daily volatility and closing prices of BSE indices.Additionally,its performance is compared with three other similarly developed models—ELM based on particle swarm optimization,genetic algorithm,and gradient descent—and find the performance of the proposed algorithm superior.Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model.Hence,this model can be used as a promising tool for financial forecasting.
基金supported by National Natural Science Foundation of China (Grant No. 61571040)
文摘According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R^2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.
