Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devi...Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.展开更多
云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此...云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memor...The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.展开更多
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on...To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.展开更多
We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-tim...We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.展开更多
文摘Load forecasting is of great significance to the development of new power systems.With the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and volatile.This places higher demands on the prediction and analysis of power loads.In order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved Whale Optimization Algorithm(IWOA)with mixed strategies was proposed.Firstly,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data itself.Then,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the BiLSTM.This allowed the model to differentiate the importance of load sequences at different time intervals.At the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the model.In this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.
文摘云计算的快速发展使得服务器面临的负载压力逐渐增加,如何精准预测负载资源成为云中心资源分配与服务器安全运行的重要课题。现有的单一模型在捕捉全局特征方面存在不足,而组合模型在处理时序数据时的平稳性和解释性方面有所欠缺。因此,提出一种基于NeuralProphet分解的卷积神经网络(CNN)-长短期记忆(LSTM)网络-注意力(Attention)机制的组合模型。NeuralProphet将负载数据分解为趋势、季节和自回归项分量,增强数据的平稳性和解释性,从而使模型能更高效地捕捉全局特征和长期依赖关系;并通过注意力机制动态权重分配,聚焦影响预测结果的关键特征,进一步提高对未来时刻的预测精度。在Alibaba Cluster Data V2018数据集上的实验结果表明,所提出的组合模型在预测精度和性能方面优于其他深度学习模型。与单一模型NeuralProphet及CNN-BiLSTM组合模型相比,该模型在R2评分上提高了17.9%,均方根误差(RMSE)降低了73.6%,平均绝对误差(MAE)降低了69.7%,对称平均绝对百分比误差(sMAPE)降低了65.3%,具备更高的预测准确性和鲁棒性,有助于提高云资源利用效率。
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
基金Project(51105170) supported by the National Natural Science Foundation of ChinaProject supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education,China
文摘The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.
基金supported by the Natural Science Basic Research Prog ram of Shaanxi(2022JQ-593)。
文摘To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
基金This project was supported by the National Natural Science Foundation of China (60074008) .
文摘We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory （BAM） neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model （SNNM）. For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities （LMIs）. We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
