This paper is concerned with the reliable H∞filtering,reliable filtering,Lyapunov function,sensor failure,linear matrix inequality(LMI)filtering problem against sensor failures for a class of discrete-time systems wi...This paper is concerned with the reliable H∞filtering,reliable filtering,Lyapunov function,sensor failure,linear matrix inequality(LMI)filtering problem against sensor failures for a class of discrete-time systems with sector-bounded nonlinearities.The resulting design is that the filtering error system is asymptotically stable and meets the prescribed H∞filtering,reliable filtering,Lyapunov function,sensor failure,linear matrix inequality(LMI)norm constraint in normal case as well as in sensor failure case.Sufficient conditions for the existence of the filter are obtained by using appropriate Lyapunov functional and linear matrix inequality(LMI)techniques.Moreover,in order to reduce the design conservativeness and get better performance,we adopt the slack variable method to realize the decoupling between the Lyapunov matrices and the system dynamic matrices.A numerical example is provided to demonstrate the effectiveness of the proposed designs.展开更多
A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy syst...A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.展开更多
The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.展开更多
基金Supported by National Basic Research Program of China(973 Program)(2009CB320604)State Key Program of National Natural Science Foundation of China(60534010)+3 种基金National Natural Science Foundation of China(60674021)Funds for Creative Research Groups of China(60821063)the 111 Project(B08015)the Funds of Doctoral Program of Ministry of Education of China(20060145019)
文摘This paper is concerned with the reliable H∞filtering,reliable filtering,Lyapunov function,sensor failure,linear matrix inequality(LMI)filtering problem against sensor failures for a class of discrete-time systems with sector-bounded nonlinearities.The resulting design is that the filtering error system is asymptotically stable and meets the prescribed H∞filtering,reliable filtering,Lyapunov function,sensor failure,linear matrix inequality(LMI)norm constraint in normal case as well as in sensor failure case.Sufficient conditions for the existence of the filter are obtained by using appropriate Lyapunov functional and linear matrix inequality(LMI)techniques.Moreover,in order to reduce the design conservativeness and get better performance,we adopt the slack variable method to realize the decoupling between the Lyapunov matrices and the system dynamic matrices.A numerical example is provided to demonstrate the effectiveness of the proposed designs.
基金Project(61473298)supported by the National Natural Science Foundation of ChinaProject(2015QNA65)supported by Fundamental Research Funds for the Central Universities,China
文摘A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.
基金Supported by National Basic Research Program of China （973 Program） （2009CB320600）, National Natural Science Foundation of China （60828007, 60534010, 60821063）, the Leverhulme Trust （F/00. 120/BC） in the United Kingdom, and the 111 Project （B08015）
基金Projects(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
