The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain c...A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain conditions is obtained. An error system around the steady state is studied further. Moreover, the condition for the state having no traffic jam is derived. A new control scheme is presented to suppress the traffic jam in the modified coupled map car-following model under the open boundary. A control signal including the velocity differences between the following and the considering vehicles, and between the preceding and the considering vehicles is used. The condition under which the traffic jam can be well suppressed is analysed. The results are compared with that presented by t^onishi et al. （the KKH model）. The simulation results show that the temporal behaviour obtained in our model is better than that in the KKH model. The simulation results are in good agreement with the theoretical analysis.展开更多
为了更准确地描述带有记忆效应的射频(RF)功放特性,基于传统的动态X参数模型,结合功放长期记忆效应以及短期记忆效应机理,提出一种新型动态X参数功放建模方法。新模型保留X参数模型的静态核函数,利用双记忆路径模型提取出表征记忆效应...为了更准确地描述带有记忆效应的射频(RF)功放特性,基于传统的动态X参数模型,结合功放长期记忆效应以及短期记忆效应机理,提出一种新型动态X参数功放建模方法。新模型保留X参数模型的静态核函数,利用双记忆路径模型提取出表征记忆效应的非线性函数,替换动态核函数。采用输出信号为幅度与频率双变量的新型反馈(FB)结构,引入时变频率变量而简化动态核函数为二维核函数。使用MW6S010N设计功放并建模,由仿真可知,新模型在单音大信号及码分多址(CDMA)信号激励下,均能正确表征功放特性,归一化均方误差(NMSE)较静态X参数模型、传统动态X参数模型以及前馈(FF)结构X参数模型分别减少8.0 d B、6.3 d B、2.5 d B。结果表明该模型能够更加准确拟合带有非线性记忆效应功率放大器的特性。展开更多
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.11072117,10802042,and 60904068)the Natural Science Foundation of Zhejiang Province,China (Grant No.Y6100023)+1 种基金the Natural Science Foundation of Ningbo,China (Grant No.2009B21003)the K.C.Wong Magna Fund in Ningbo University,China
文摘A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain conditions is obtained. An error system around the steady state is studied further. Moreover, the condition for the state having no traffic jam is derived. A new control scheme is presented to suppress the traffic jam in the modified coupled map car-following model under the open boundary. A control signal including the velocity differences between the following and the considering vehicles, and between the preceding and the considering vehicles is used. The condition under which the traffic jam can be well suppressed is analysed. The results are compared with that presented by t^onishi et al. （the KKH model）. The simulation results show that the temporal behaviour obtained in our model is better than that in the KKH model. The simulation results are in good agreement with the theoretical analysis.
文摘为了更准确地描述带有记忆效应的射频(RF)功放特性,基于传统的动态X参数模型,结合功放长期记忆效应以及短期记忆效应机理,提出一种新型动态X参数功放建模方法。新模型保留X参数模型的静态核函数,利用双记忆路径模型提取出表征记忆效应的非线性函数,替换动态核函数。采用输出信号为幅度与频率双变量的新型反馈(FB)结构,引入时变频率变量而简化动态核函数为二维核函数。使用MW6S010N设计功放并建模,由仿真可知,新模型在单音大信号及码分多址(CDMA)信号激励下,均能正确表征功放特性,归一化均方误差(NMSE)较静态X参数模型、传统动态X参数模型以及前馈(FF)结构X参数模型分别减少8.0 d B、6.3 d B、2.5 d B。结果表明该模型能够更加准确拟合带有非线性记忆效应功率放大器的特性。
