Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate syst...Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.展开更多
线性自抗扰控制(linear active disturbance rejection control,LADRC)是不依赖于被控对象的数学模型,在工业过程中具有极大的应用前景,LADRC参数整定是其在工业过程中能否应用的重要环节.鉴于实际工业控制中大都采用PID控制器,通过对二...线性自抗扰控制(linear active disturbance rejection control,LADRC)是不依赖于被控对象的数学模型,在工业过程中具有极大的应用前景,LADRC参数整定是其在工业过程中能否应用的重要环节.鉴于实际工业控制中大都采用PID控制器,通过对二阶LADRC结构与其状态观测器的传递函数进行分析,得到二阶LADRC与PID控制器具有较强的联系,且LADRC比PID有着更好的控制性能.提出一种通过现有PID参数直接得到LADRC参数初始值的方法,以达到更好的控制性能,并基于一阶惯性加时延模型,得到将现有PID整定方法转化为二阶线性自抗扰控制参数整定方法.最后通过基准系统仿真表明所提出方法的有效性.展开更多
The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results...The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.展开更多
By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the S...By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.展开更多
Exponential stability of the first order singular distributed parameter systems is discussedin the light of degenerate semi-group methods,which is described by the abstract developing equationin Hilbert space.The nece...Exponential stability of the first order singular distributed parameter systems is discussedin the light of degenerate semi-group methods,which is described by the abstract developing equationin Hilbert space.The necessary and sufficient conditions concerning the exponential stability of thefirst order singular distributed parameter systems are given.展开更多
基金Supported by the Natural Sciences and Engineering Research Council of Canada and National Natural Science Foundation of P.R.China
文摘Multirate systems are abundant in industry; for example, many soft-sensor design problems are related to modeling, parameter identification, or state estimation involving multirate systems. The study of multirate systems goes back to the early 1950's, and has become an active research area in systems and control. This paper briefly surveys the history of development in the area of multirate systems, and introduces some basic concepts and latest results on multirate systems, including a polynomial transformation technique and the lifting technique as tools for handling multirate systems, lifted state space models, parameter identification of dual-rate systems, how to determine fast single-rate models from dual-rate models and directly from dual-rate data, and a hierarchical identification method for general multirate systems. Finally, some further research topics for multirate systems are given.
文摘线性自抗扰控制(linear active disturbance rejection control,LADRC)是不依赖于被控对象的数学模型,在工业过程中具有极大的应用前景,LADRC参数整定是其在工业过程中能否应用的重要环节.鉴于实际工业控制中大都采用PID控制器,通过对二阶LADRC结构与其状态观测器的传递函数进行分析,得到二阶LADRC与PID控制器具有较强的联系,且LADRC比PID有着更好的控制性能.提出一种通过现有PID参数直接得到LADRC参数初始值的方法,以达到更好的控制性能,并基于一阶惯性加时延模型,得到将现有PID整定方法转化为二阶线性自抗扰控制参数整定方法.最后通过基准系统仿真表明所提出方法的有效性.
基金the National Nature Science Foundation of China (60775047, 60402024)
文摘The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
基金supported by National Natural Science Foundation of China(61273108)the Fundamental Research Funds for the Central Universities(106112013CDJZR175501)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry
基金Supported by the National Natural Science Foundation of China (Grant Nos. 60574051 and 60674092) the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2007017) and by Program for Innovative Research Team of Jiangnan University
文摘By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.
基金This research is supported by the National Natural Science Foundation of China under Grant No.60674018.
文摘Exponential stability of the first order singular distributed parameter systems is discussedin the light of degenerate semi-group methods,which is described by the abstract developing equationin Hilbert space.The necessary and sufficient conditions concerning the exponential stability of thefirst order singular distributed parameter systems are given.
