Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs ...Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.展开更多
Abstract In this paper, we introduce several on-going research projects to support parallel and distribut,ed computing on heterogeneous networks of workstations (NOW) in the High Performance Computing and Software Lah...Abstract In this paper, we introduce several on-going research projects to support parallel and distribut,ed computing on heterogeneous networks of workstations (NOW) in the High Performance Computing and Software Lahoratory at the University of Texas at San Antonio. The projects at aiming at addressing three technical issues. First, the factors of heterogeneity and time-sharing effects make traditional performance models/metrics for homogeneous computing performance measurement and evaluation not. suitable for bet.erogeneous computing. We develop practical models and metrics which quantify. the heterogeneity of networks and characterize the performance effects. Second, in order to perform parallel computation effectively, special system support is necessary. We are developing system schemes for heterogeneity management, process scheduling and efficient communications. Finally, to provide insight into system performance, we are developing two types of supporting tools : a graphical instrumentation monitor to aid users in investigating performance problems and in determining the most effective way of exploiting the NOW systems, and a trace-driven simulator to test and compare different system management and scheduling schemes.展开更多
More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven...More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming,while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation.Therefore,it is a future development trend of transient stability assessment methods to combine these two kinds of methods.In this paper,the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage,and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage.In order to quantify the credibility level of the data-driven methods,the credibility index of the output results is proposed.Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index.Thus,a newparallel integratedmodel-driven and datadriven online transient stability assessment method is proposed.The accuracy,efficiency,and adaptability of the proposed method are verified by numerical examples.展开更多
A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is ...A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.展开更多
In this paper,we introduce the design and implementation of ParaVT,which is a visual performance analysis and parallel debugging tool.In ParaVT,we propose an automated instrumentation mechanism.Based on this mechanism...In this paper,we introduce the design and implementation of ParaVT,which is a visual performance analysis and parallel debugging tool.In ParaVT,we propose an automated instrumentation mechanism.Based on this mechanism,ParaVT automatically analyzes the performance bottleneck of parallel applications and provides a visual user interface to monitor and analyze the performance of parallel programs.In addition,it also supports certain extensions.展开更多
基金Project(2002CB312200) supported by the National Key Fundamental Research and Development Program of China project(60574019) supported by the National Natural Science Foundation of China
文摘Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.
文摘Abstract In this paper, we introduce several on-going research projects to support parallel and distribut,ed computing on heterogeneous networks of workstations (NOW) in the High Performance Computing and Software Lahoratory at the University of Texas at San Antonio. The projects at aiming at addressing three technical issues. First, the factors of heterogeneity and time-sharing effects make traditional performance models/metrics for homogeneous computing performance measurement and evaluation not. suitable for bet.erogeneous computing. We develop practical models and metrics which quantify. the heterogeneity of networks and characterize the performance effects. Second, in order to perform parallel computation effectively, special system support is necessary. We are developing system schemes for heterogeneity management, process scheduling and efficient communications. Finally, to provide insight into system performance, we are developing two types of supporting tools : a graphical instrumentation monitor to aid users in investigating performance problems and in determining the most effective way of exploiting the NOW systems, and a trace-driven simulator to test and compare different system management and scheduling schemes.
基金funded by the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.(Project No.520530200013).
文摘More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming,while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation.Therefore,it is a future development trend of transient stability assessment methods to combine these two kinds of methods.In this paper,the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage,and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage.In order to quantify the credibility level of the data-driven methods,the credibility index of the output results is proposed.Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index.Thus,a newparallel integratedmodel-driven and datadriven online transient stability assessment method is proposed.The accuracy,efficiency,and adaptability of the proposed method are verified by numerical examples.
文摘数据驱动的多元化发展导致数据异构性增强、维度提升和特征量规模扩大,给贸易经济分析带来更大挑战。为了提高贸易经济分析的科学性,采用非平行超平面支持向量机算法(support vector machine,SVM)对贸易经济进行预测分析。首先,根据贸易经济影响因素进行主成分分析,获取影响贸易经济的关键特征,并对特征进行量化和去噪处理。然后,采用广义特征值最接近支持向量机(proximal support vector machine via generalized eigenvalues,GEPSVM)进行贸易经济预测分类。根据预测指标要求,选择核函数GEPSVM算法(KGEPSVM算法)对分类的非平行超平面求解,通过类别划分函数获得经济预测结果。实证分析表明,对比常用的非平行超平面支持向量机算法,所提算法的贸易经济预测性能更优,而且在常用贸易经济指标的预测中,表现出较高预测精度和稳定性。
文摘为提升并联式混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,针对等效燃油消耗最小控制策略(equivalent fuel consumption minimum strategy,ECMS)在不同工况下适应性差的问题,以优化整车等效燃油消耗量为目标,设计基于工况识别算法的变等效因子ECMS能量管理策略。选取3类典型工况建立支持向量机分类模型,通过递归特征消除法对样本特征进行选择,采用鲸鱼算法对支持向量机进行参数优化,使用模拟退火算法分别对3类工况的ECMS等效因子进行离线全局最优求解,并分别存储于等效因子库中,通过训练好的支持向量机分类器对目标优化工况进行工况识别,不同类型的工况片段采用不同的等效因子进行转矩分配。仿真结果显示:相比于逻辑门限能量管理策略,基于工况识别算法的变等效因子ECMS能量管理策略的电池荷电状态(state of charge,SOC)变化量减少8.67%,节油率为13.11%;相比于优化前的ECMS策略电池SOC变化量减少3.47%,节油率约为6.63%。本文提出的基于工况识别算法的变等效因子ECMS能量管理策略可以有效地减少燃油消耗量,提升PHEV的整车经济性。
基金supported by the National Defense Preliminary Research Program of China(A157167)the National Defense Fundamental of China(9140A19030314JB35275)
文摘A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.
文摘In this paper,we introduce the design and implementation of ParaVT,which is a visual performance analysis and parallel debugging tool.In ParaVT,we propose an automated instrumentation mechanism.Based on this mechanism,ParaVT automatically analyzes the performance bottleneck of parallel applications and provides a visual user interface to monitor and analyze the performance of parallel programs.In addition,it also supports certain extensions.
