Based on the topological analysis of three-phase matrix AC to AC conversion circuit, an AC to AC nine-switch matrix isequivalent to rectification part and conversion part. The Matrix converter can be viewed as AC-DC-A...Based on the topological analysis of three-phase matrix AC to AC conversion circuit, an AC to AC nine-switch matrix isequivalent to rectification part and conversion part. The Matrix converter can be viewed as AC-DC-AC converter, the asymmetricregular sampling method SPWM(Sine Pulse Width Modulation) is studied and applied in the three-phase matrix AC to AC converter,Based on Matlab/simulink the simulation of the matrix converter with such strategy is carried out. Inductive load simulation is carriedout on the matrix converter prototype. The simulation results verify the workability of the asymmetric regular sampling method SPWMstrategy for matrix converter.展开更多
Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes.Recent studies on deep neural networks(DNNs)suggest that such sample differences are roo...Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes.Recent studies on deep neural networks(DNNs)suggest that such sample differences are rooted in the distribution of intrinsic pattern information,namely sample regularity.Motivated by recent discoveries in network memorization and generalization,we propose a pair of sample regularity measures with a formulation-consistent representation for both processes.Specifically,the cumulative binary training/generalizing loss(CBTL/CBGL),the cumulative number of correct classifications of the training/test sample within the training phase,is proposed to quantify the stability in the memorization-generalization process,while forgetting/mal-generalizing events(ForEvents/MgEvents),i.e.,the misclassification of previously learned or generalized samples,are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics.The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent(SGD)optimization are validated through sample-wise analyses.Further training/test sample selection applications show that the proposed measures,which share the unified computing procedure,could benefit both tasks.展开更多
文摘Based on the topological analysis of three-phase matrix AC to AC conversion circuit, an AC to AC nine-switch matrix isequivalent to rectification part and conversion part. The Matrix converter can be viewed as AC-DC-AC converter, the asymmetricregular sampling method SPWM(Sine Pulse Width Modulation) is studied and applied in the three-phase matrix AC to AC converter,Based on Matlab/simulink the simulation of the matrix converter with such strategy is carried out. Inductive load simulation is carriedout on the matrix converter prototype. The simulation results verify the workability of the asymmetric regular sampling method SPWMstrategy for matrix converter.
基金supported by the National Natural Science Foundation of China(No.62202370).
文摘Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes.Recent studies on deep neural networks(DNNs)suggest that such sample differences are rooted in the distribution of intrinsic pattern information,namely sample regularity.Motivated by recent discoveries in network memorization and generalization,we propose a pair of sample regularity measures with a formulation-consistent representation for both processes.Specifically,the cumulative binary training/generalizing loss(CBTL/CBGL),the cumulative number of correct classifications of the training/test sample within the training phase,is proposed to quantify the stability in the memorization-generalization process,while forgetting/mal-generalizing events(ForEvents/MgEvents),i.e.,the misclassification of previously learned or generalized samples,are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics.The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent(SGD)optimization are validated through sample-wise analyses.Further training/test sample selection applications show that the proposed measures,which share the unified computing procedure,could benefit both tasks.
