Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa...Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.展开更多
针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algori...针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algorithm and red fox algorithm,HNSGA2RFA),用于解决多目标的柔性流水车间调度问题。通过ROV规则实现GA和RFA的编码转换,并提出了归一化分组策略(normalized grouping strategy)。试验结果表明,HNSGA2RFA算法在优化速度和最优解集数量上均优于原NSGA-Ⅱ算法。展开更多
文摘Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
文摘针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algorithm and red fox algorithm,HNSGA2RFA),用于解决多目标的柔性流水车间调度问题。通过ROV规则实现GA和RFA的编码转换,并提出了归一化分组策略(normalized grouping strategy)。试验结果表明,HNSGA2RFA算法在优化速度和最优解集数量上均优于原NSGA-Ⅱ算法。
