The Bayes estimator of the parameter is obtained for the scale exponential family in the case of identically distributed and positively associated（PA） samples under weighted square loss function.We construct the emp...The Bayes estimator of the parameter is obtained for the scale exponential family in the case of identically distributed and positively associated（PA） samples under weighted square loss function.We construct the empirical Bayes（EB） estimator and prove it is asymptotic optimal.展开更多
Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled mul...Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.展开更多
This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and glob...This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.展开更多
基金Supported by the Anhui University of Technology and Science Foundation for the Recruiting Talent（2009YQ005） Acknowledgements The authors thank the referee for his/her careful reading of the manuscript and many useful suggestions.
文摘The Bayes estimator of the parameter is obtained for the scale exponential family in the case of identically distributed and positively associated（PA） samples under weighted square loss function.We construct the empirical Bayes（EB） estimator and prove it is asymptotic optimal.
文摘Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.
基金supported by National Natural Science Foundation of China under Grant 61573005 and 11361010the Foundation for Young Professors of Jimei Universitythe Foundation of Fujian Higher Education(JA11154,JA11144)
文摘This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.
