In this work,we investigate a classical pseudomonotone and Lipschitz continuous variational inequality in the setting of Hilbert space,and present a projection-type approximation method for solving this problem.Our me...In this work,we investigate a classical pseudomonotone and Lipschitz continuous variational inequality in the setting of Hilbert space,and present a projection-type approximation method for solving this problem.Our method requires only to compute one projection onto the feasible set per iteration and without any linesearch procedure or additional projections as well as does not need to the prior knowledge of the Lipschitz constant and the sequentially weakly continuity of the variational inequality mapping.A strong convergence is established for the proposed method to a solution of a variational inequality problem under certain mild assumptions.Finally,we give some numerical experiments illustrating the performance of the proposed method for variational inequality problems.展开更多
This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietn...This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.展开更多
基金funded by National University ofCivil Engineering(NUCE)under grant number 15-2020/KHXD-TD。
文摘In this work,we investigate a classical pseudomonotone and Lipschitz continuous variational inequality in the setting of Hilbert space,and present a projection-type approximation method for solving this problem.Our method requires only to compute one projection onto the feasible set per iteration and without any linesearch procedure or additional projections as well as does not need to the prior knowledge of the Lipschitz constant and the sequentially weakly continuity of the variational inequality mapping.A strong convergence is established for the proposed method to a solution of a variational inequality problem under certain mild assumptions.Finally,we give some numerical experiments illustrating the performance of the proposed method for variational inequality problems.
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant Number[105.99-2016.05].
文摘This study proposed a novel object-based hybrid classification model named GMNN that combines Grasshopper Optimization Algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,Vietnam.Four bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics software.These segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed model.Alternatively,three training and validation datasets of different sizes were used to verify the robustness of this model.For all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was 0.97.Also,the performance of this model was examined by comparing several statistical indicators with common benchmark classifiers.The results showed that GMNN out-performed established methods in all comparable indicators.These results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover studies.In a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.
